Triple
T19587370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yerevan Metro |
E470137
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Barekamutyun station
Barekamutyun station is a key underground stop on the Yerevan Metro system in Armenia’s capital city.
|
E1385588
|
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: Barekamutyun station | Statement: [Yerevan Metro, hasStation, Barekamutyun station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barekamutyun station Context triple: [Yerevan Metro, hasStation, Barekamutyun station]
-
A.
Hankar station
Hankar station is a Brussels Metro station on the city's Line 5, serving the Auderghem municipality in southeastern Brussels.
-
B.
Imbiah station
Imbiah station is a monorail station on Singapore’s Sentosa Island serving the Imbiah attractions area along the Sentosa Express line.
-
C.
Nanlishilu station
Nanlishilu station is a subway station on Line 1 of the Beijing Subway serving the Nanlishi Road area in central Beijing.
-
D.
Batutulis Station
Batutulis Station is a small railway station in Bogor, West Java, Indonesia, serving local commuter and regional train services on the line south of Bogor.
-
E.
Sucat station
Sucat station is a commuter rail station in Muntinlupa, Metro Manila, serving passengers on the Philippine National Railways network.
- 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: Barekamutyun station Triple: [Yerevan Metro, hasStation, Barekamutyun station]
Generated description
Barekamutyun station is a key underground stop on the Yerevan Metro system in Armenia’s capital city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Barekamutyun station Target entity description: Barekamutyun station is a key underground stop on the Yerevan Metro system in Armenia’s capital city.
-
A.
Hankar station
Hankar station is a Brussels Metro station on the city's Line 5, serving the Auderghem municipality in southeastern Brussels.
-
B.
Imbiah station
Imbiah station is a monorail station on Singapore’s Sentosa Island serving the Imbiah attractions area along the Sentosa Express line.
-
C.
Nanlishilu station
Nanlishilu station is a subway station on Line 1 of the Beijing Subway serving the Nanlishi Road area in central Beijing.
-
D.
Batutulis Station
Batutulis Station is a small railway station in Bogor, West Java, Indonesia, serving local commuter and regional train services on the line south of Bogor.
-
E.
Sucat station
Sucat station is a commuter rail station in Muntinlupa, Metro Manila, serving passengers on the Philippine National Railways network.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64052f61c81908bb49927d4246030 |
completed | April 20, 2026, 3:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a075f12fe008190bf2aaf7c63c44467 |
completed | May 15, 2026, 5:59 p.m. |
| NEDg | Description generation | batch_6a0760583b588190b8f648acc26bc1ae |
completed | May 15, 2026, 6:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0761223cc081908e08a33eda54d222 |
completed | May 15, 2026, 6:08 p.m. |
Created at: April 10, 2026, 1:43 p.m.