Triple
T14518071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bangalore Metro |
E340575
|
entity |
| Predicate | hasDepot |
P2413
|
FINISHED |
| Object |
Kengeri depot
Kengeri depot is a maintenance and stabling facility serving the western end of the Namma Metro network in Bengaluru, India.
|
E1104866
|
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: Kengeri depot | Statement: [Bangalore Metro, hasDepot, Kengeri depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kengeri depot Context triple: [Bangalore Metro, hasDepot, Kengeri depot]
-
A.
Peenya depot
Peenya depot is a major maintenance and stabling facility serving Bengaluru’s Namma Metro system.
-
B.
Byappanahalli depot
Byappanahalli depot is a major maintenance and stabling facility serving Bengaluru’s Namma Metro system, located near the Byappanahalli metro station.
-
C.
Koyambedu depot
Koyambedu depot is a major maintenance, operations, and stabling facility serving the Chennai Metro rail network in Chennai, India.
-
D.
Kurla bus depot
Kurla bus depot is a major public bus terminal in the Kurla area of Mumbai, serving as an important hub for local and regional bus services.
-
E.
Wimco Nagar depot
Wimco Nagar depot is a maintenance and stabling facility serving the northern stretch of the Chennai Metro network in Chennai, 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: Kengeri depot Triple: [Bangalore Metro, hasDepot, Kengeri depot]
Generated description
Kengeri depot is a maintenance and stabling facility serving the western end of the Namma Metro network in Bengaluru, India.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kengeri depot Target entity description: Kengeri depot is a maintenance and stabling facility serving the western end of the Namma Metro network in Bengaluru, India.
-
A.
Peenya depot
Peenya depot is a major maintenance and stabling facility serving Bengaluru’s Namma Metro system.
-
B.
Byappanahalli depot
Byappanahalli depot is a major maintenance and stabling facility serving Bengaluru’s Namma Metro system, located near the Byappanahalli metro station.
-
C.
Koyambedu depot
Koyambedu depot is a major maintenance, operations, and stabling facility serving the Chennai Metro rail network in Chennai, India.
-
D.
Kurla bus depot
Kurla bus depot is a major public bus terminal in the Kurla area of Mumbai, serving as an important hub for local and regional bus services.
-
E.
Wimco Nagar depot
Wimco Nagar depot is a maintenance and stabling facility serving the northern stretch of the Chennai Metro network in Chennai, India.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de9a6f50208190b687b505f5cd1aa2 |
completed | April 14, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a4b71688190ae9ebccdc81d09f8 |
completed | May 8, 2026, 5:53 a.m. |
| NEDg | Description generation | batch_69fd7bf6b13481908307a2037d0de804 |
completed | May 8, 2026, 6 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd7ce0d8a0819083ba348412d76ee5 |
completed | May 8, 2026, 6:04 a.m. |
Created at: April 10, 2026, 1:22 a.m.