Triple
T1622705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mumbai City FC |
E35067
|
entity |
| Predicate | notablePlayer |
P304
|
FINISHED |
| Object |
Sunil Chhetri
Sunil Chhetri is an Indian professional footballer widely regarded as one of the country’s greatest players and among the world’s leading international goal scorers.
|
E185110
|
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: Sunil Chhetri | Statement: [Mumbai City FC, notablePlayer, Sunil Chhetri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sunil Chhetri Context triple: [Mumbai City FC, notablePlayer, Sunil Chhetri]
-
A.
Sourav Pal
Sourav Pal is an accomplished Indian chemist and academic known for his contributions to theoretical and computational chemistry.
-
B.
Vijay Kumar
Vijay Kumar is a prominent roboticist and engineer known for his pioneering work in multi-robot systems and aerial robotics.
-
C.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
-
D.
Madanjeet Singh
Madanjeet Singh was an Indian diplomat, artist, and UNESCO Goodwill Ambassador known for his lifelong advocacy of peace, tolerance, and non-violence.
-
E.
Rajat Monga
Rajat Monga is a computer scientist and engineer best known as a co-creator and early lead of TensorFlow at Google Brain.
- 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: Sunil Chhetri Triple: [Mumbai City FC, notablePlayer, Sunil Chhetri]
Generated description
Sunil Chhetri is an Indian professional footballer widely regarded as one of the country’s greatest players and among the world’s leading international goal scorers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sunil Chhetri Target entity description: Sunil Chhetri is an Indian professional footballer widely regarded as one of the country’s greatest players and among the world’s leading international goal scorers.
-
A.
Sourav Pal
Sourav Pal is an accomplished Indian chemist and academic known for his contributions to theoretical and computational chemistry.
-
B.
Vijay Kumar
Vijay Kumar is a prominent roboticist and engineer known for his pioneering work in multi-robot systems and aerial robotics.
-
C.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
-
D.
Madanjeet Singh
Madanjeet Singh was an Indian diplomat, artist, and UNESCO Goodwill Ambassador known for his lifelong advocacy of peace, tolerance, and non-violence.
-
E.
Rajat Monga
Rajat Monga is a computer scientist and engineer best known as a co-creator and early lead of TensorFlow at Google Brain.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a909cf3c7481909ddbe6a6596bb0c8 |
completed | March 5, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58ccc80c819088ecd91f0a99a247 |
completed | March 8, 2026, 11:09 a.m. |
| NEDg | Description generation | batch_69ad5a619da481908d66837ea94c91cf |
completed | March 8, 2026, 11:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad5b41a68c8190ba293d8e8c35521b |
completed | March 8, 2026, 11:19 a.m. |
Created at: March 4, 2026, 7:28 p.m.