Triple
T3515772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turin Metro |
E74302
|
entity |
| Predicate | line |
P1293
|
FINISHED |
| Object |
Line 1
Line 1 is the first and main automated metro line of the Turin Metro system in Turin, Italy, connecting key areas of the city along an underground route.
|
E365560
|
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: Line 1 | Statement: [Turin Metro, line, Line 1]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 1 Context triple: [Turin Metro, line, Line 1]
-
A.
Line 1
Line 1 is the oldest and one of the busiest lines of the Santiago Metro, running primarily east–west across central Santiago, Chile.
-
B.
Line 1
Line 1 is the oldest and one of the busiest lines of the Mexico City Metro, running east–west across the city and serving many central, high-traffic stations.
-
C.
Line 1
Line 1 is a major north–south rapid transit line of the Shanghai Metro and one of the system’s oldest and busiest routes.
-
D.
Line 1
Line 1 is one of the main lines of the Barcelona Metro rapid transit system, running on a largely east–west axis and serving several key districts of the city.
-
E.
Line 1
Line 1 is the first operational corridor of the Mumbai Monorail system, serving as a key elevated transit route in Mumbai, India.
- 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: Line 1 Triple: [Turin Metro, line, Line 1]
Generated description
Line 1 is the first and main automated metro line of the Turin Metro system in Turin, Italy, connecting key areas of the city along an underground route.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 1 Target entity description: Line 1 is the first and main automated metro line of the Turin Metro system in Turin, Italy, connecting key areas of the city along an underground route.
-
A.
Line 1
Line 1 is one of the main lines of the Barcelona Metro rapid transit system, running on a largely east–west axis and serving several key districts of the city.
-
B.
Line 1
Line 1 is the main north–south route of the Tehran Metro system, serving as one of its busiest and most important rapid transit lines.
-
C.
Line 1
Line 1 is a major east–west rapid transit route of the Brussels Metro system, connecting key districts across the Belgian capital.
-
D.
Line 1
Line 1 is a major north–south rapid transit line of the Shanghai Metro and one of the system’s oldest and busiest routes.
-
E.
Line 1
Line 1 is the oldest and one of the busiest lines of the Paris Métro, running primarily east–west through central Paris and serving many major landmarks.
- 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_69ad85cfb5c881909c9a2edd9d6043cc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc30362c81908ca7497a6a935cc6 |
completed | March 8, 2026, 6:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e7a5ab08190971c9dfd6550eb2d |
completed | March 13, 2026, 3:03 a.m. |
| NEDg | Description generation | batch_69b37ff7b1c8819085b16ea9eb9175eb |
completed | March 13, 2026, 3:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3808903e481908d312766e126204f |
completed | March 13, 2026, 3:12 a.m. |
Created at: March 8, 2026, 3:19 p.m.