Triple
T4185104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ahmedabad Metro |
E88290
|
entity |
| Predicate | depot |
P14646
|
FINISHED |
| Object |
Gyaspur Depot
Gyaspur Depot is a major maintenance and stabling facility serving the Ahmedabad Metro system in Ahmedabad, India.
|
E418890
|
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: Gyaspur Depot | Statement: [Ahmedabad Metro, depot, Gyaspur Depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gyaspur Depot Context triple: [Ahmedabad Metro, depot, Gyaspur Depot]
-
A.
Wadala Depot
Wadala Depot is a key station and maintenance hub on the Mumbai Monorail network, serving the Wadala area of Mumbai, India.
-
B.
Nakamozu Depot
Nakamozu Depot is a major rail yard and maintenance facility serving Osaka Metro’s Midosuji Line in Osaka, Japan.
-
C.
Saginuma Depot
Saginuma Depot is a maintenance and storage facility for Tokyo Metro trains serving the Hanzomon Line in the Tokyo area.
-
D.
Kasukabe Depot
Kasukabe Depot is a railway maintenance and storage facility in Kasukabe, Saitama Prefecture, serving Tokyo Metro’s subway rolling stock.
-
E.
Suminoe Depot
Suminoe Depot is a railway maintenance and storage facility serving Osaka Metro’s Yotsubashi Line in Osaka, Japan.
- 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: Gyaspur Depot Triple: [Ahmedabad Metro, depot, Gyaspur Depot]
Generated description
Gyaspur Depot is a major maintenance and stabling facility serving the Ahmedabad Metro system in Ahmedabad, India.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gyaspur Depot Target entity description: Gyaspur Depot is a major maintenance and stabling facility serving the Ahmedabad Metro system in Ahmedabad, India.
-
A.
Wadala Depot
Wadala Depot is a key station and maintenance hub on the Mumbai Monorail network, serving the Wadala area of Mumbai, India.
-
B.
Nakamozu Depot
Nakamozu Depot is a major rail yard and maintenance facility serving Osaka Metro’s Midosuji Line in Osaka, Japan.
-
C.
Saginuma Depot
Saginuma Depot is a maintenance and storage facility for Tokyo Metro trains serving the Hanzomon Line in the Tokyo area.
-
D.
Kasukabe Depot
Kasukabe Depot is a railway maintenance and storage facility in Kasukabe, Saitama Prefecture, serving Tokyo Metro’s subway rolling stock.
-
E.
Suminoe Depot
Suminoe Depot is a railway maintenance and storage facility serving Osaka Metro’s Yotsubashi Line in Osaka, Japan.
- 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_69aed9477e8c81908bcb862d2db55b1d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af032318bc81908345db0753f8ba66 |
completed | March 9, 2026, 5:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589fe4b508190a1c5a1d426245ede |
completed | March 14, 2026, 4:17 p.m. |
| NEDg | Description generation | batch_69b58a64e67081909d9c03c8f92b4f7e |
completed | March 14, 2026, 4:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b58aeb4f848190aa8d8511c3b63eaa |
completed | March 14, 2026, 4:20 p.m. |
Created at: March 9, 2026, 3:45 p.m.