Triple
T7694284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shah Rukh Khan |
E174331
|
entity |
| Predicate | coOwnerWith |
P3498
|
FINISHED |
| Object | Jay Mehta |
E640144
|
NE FINISHED |
How this triple was built (2 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: Jay Mehta | Statement: [Shah Rukh Khan, coOwnerWith, Jay Mehta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jay Mehta Context triple: [Shah Rukh Khan, coOwnerWith, Jay Mehta]
-
A.
Jay Mehta
chosen
Jay Mehta is an Indian businessman and industrialist, known for his interests in cement and other industries and for being married to actress Juhi Chawla.
-
B.
Nick Mehta
Nick Mehta is a technology executive best known as the CEO of Gainsight and a prominent advocate and thought leader in the field of customer success.
-
C.
Sachit Mehra
Sachit Mehra is a Canadian political figure who serves in a top leadership role within the Liberal Party of Canada.
-
D.
Rahul Bhatia
Rahul Bhatia is an Indian businessman best known as the co-founder and key architect of IndiGo’s rise into India’s largest low-cost airline.
-
E.
Deepak Nayyar
Deepak Nayyar is an Indian economist and academic known for his work on development economics and his leadership roles in major universities and international economic institutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702459f988190bf7087bf51d5317f |
completed | March 27, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b4fda81881908144cebdd2696e63 |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:02 p.m.