Triple
T542634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bengaluru |
E12663
|
entity |
| Predicate | hasMetroSystem |
P522
|
FINISHED |
| Object |
Namma Metro
Namma Metro is the rapid transit system serving Bengaluru, India, providing urban rail connectivity across the city.
|
E68080
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Namma Metro | Statement: [Bengaluru, hasMetroSystem, Namma Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Namma Metro Context triple: [Bengaluru, hasMetroSystem, Namma Metro]
-
A.
Mumbai Metro
Mumbai Metro is a rapid transit system serving the Mumbai metropolitan region, designed to alleviate congestion and complement the city’s suburban railway network.
-
B.
Masku
Masku is a municipality in Southwest Finland known for its historical estates and proximity to the city of Turku.
-
C.
Tren Urbano
Tren Urbano is a rapid transit rail system serving the San Juan metropolitan area in Puerto Rico, providing urban mass transportation across key municipalities.
-
D.
Delhi Metro
Delhi Metro is a rapid transit system serving Delhi and its surrounding metropolitan region, known for its extensive network, modern infrastructure, and role in easing urban congestion.
-
E.
Swayam
Swayam is an Indian government-backed online learning platform that provides free Massive Open Online Courses (MOOCs) from schools, colleges, and universities across the country.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Namma Metro Triple: [Bengaluru, hasMetroSystem, Namma Metro]
Generated description
Namma Metro is the rapid transit system serving Bengaluru, India, providing urban rail connectivity across the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Namma Metro Target entity description: Namma Metro is the rapid transit system serving Bengaluru, India, providing urban rail connectivity across the city.
-
A.
Mumbai Metro
Mumbai Metro is a rapid transit system serving the Mumbai metropolitan region, designed to alleviate congestion and complement the city’s suburban railway network.
-
B.
Masku
Masku is a municipality in Southwest Finland known for its historical estates and proximity to the city of Turku.
-
C.
Tren Urbano
Tren Urbano is a rapid transit rail system serving the San Juan metropolitan area in Puerto Rico, providing urban mass transportation across key municipalities.
-
D.
Delhi Metro
Delhi Metro is a rapid transit system serving Delhi and its surrounding metropolitan region, known for its extensive network, modern infrastructure, and role in easing urban congestion.
-
E.
Swayam
Swayam is an Indian government-backed online learning platform that provides free Massive Open Online Courses (MOOCs) from schools, colleges, and universities across the country.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a49334226c81908b0ea1689ef6aa3f |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4986250508190ac91cfdf7d57073d |
completed | March 1, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4cc5d690881908742b313f28a0012 |
completed | March 1, 2026, 11:31 p.m. |
| NEDg | Description generation | batch_69a4cea9d11881908f4bac61c7e63e82 |
completed | March 1, 2026, 11:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a4cf5649588190949250ee800d921f |
completed | March 1, 2026, 11:44 p.m. |
Created at: March 1, 2026, 7:32 p.m.