Triple
T20083949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thapar |
E500073
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object |
Sumeet Thapar
Sumeet Thapar is an individual associated with the use or application of the Thapar system, tool, or concept, likely in a professional or academic context.
|
E1417341
|
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: Sumeet Thapar | Statement: [Thapar, usedBy, Sumeet Thapar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sumeet Thapar Context triple: [Thapar, usedBy, Sumeet Thapar]
-
A.
Sanjay Thapar
Sanjay Thapar is an individual associated with the use or application of something created or provided by Thapar, though specific public details about him are limited.
-
B.
Amarjeet Sohi
Amarjeet Sohi is a Canadian politician who serves as the mayor of Edmonton and is a former federal cabinet minister.
-
C.
Vikram Kumar
Vikram Kumar is an Indian film director and screenwriter known for his work in Tamil and Telugu cinema, including acclaimed films like "24" and "Manam."
-
D.
Charanjit Jutla
Charanjit Jutla is a cryptographer known for his research contributions in theoretical computer science and cryptographic protocols.
-
E.
Ravi Thapar
Ravi Thapar is an Indian politician who has served as a member of the Indian National Congress and held various public offices.
- 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: Sumeet Thapar Triple: [Thapar, usedBy, Sumeet Thapar]
Generated description
Sumeet Thapar is an individual associated with the use or application of the Thapar system, tool, or concept, likely in a professional or academic context.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sumeet Thapar Target entity description: Sumeet Thapar is an individual associated with the use or application of the Thapar system, tool, or concept, likely in a professional or academic context.
-
A.
Sanjay Thapar
Sanjay Thapar is an individual associated with the use or application of something created or provided by Thapar, though specific public details about him are limited.
-
B.
Amarjeet Sohi
Amarjeet Sohi is a Canadian politician who serves as the mayor of Edmonton and is a former federal cabinet minister.
-
C.
Vikram Kumar
Vikram Kumar is an Indian film director and screenwriter known for his work in Tamil and Telugu cinema, including acclaimed films like "24" and "Manam."
-
D.
Charanjit Jutla
Charanjit Jutla is a cryptographer known for his research contributions in theoretical computer science and cryptographic protocols.
-
E.
Ravi Thapar
Ravi Thapar is an Indian politician who has served as a member of the Indian National Congress and held various public offices.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6655a2d2c81908a6b8fd2f209a825 |
completed | April 20, 2026, 5:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0843d8894481908f78e092417a8602 |
completed | May 16, 2026, 10:15 a.m. |
| NEDg | Description generation | batch_6a0844a1f6988190bbab91201b2a0d7e |
completed | May 16, 2026, 10:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0845700ec48190bb59acff92e4e616 |
completed | May 16, 2026, 10:22 a.m. |
Created at: April 11, 2026, 3:41 p.m.