Triple
T20170767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kishore Kumar |
E491950
|
entity |
| Predicate | sibling |
P363
|
FINISHED |
| Object | Ashok Kumar |
—
|
NE NERFINISHED |
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: Ashok Kumar | Statement: [Kishore Kumar, sibling, Ashok Kumar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashok Kumar Context triple: [Kishore Kumar, sibling, Ashok Kumar]
-
A.
Ashok Kumar
chosen
Ashok Kumar was a pioneering and acclaimed Indian film actor, often regarded as one of the first superstars of Hindi cinema.
-
B.
Ashok Chandra
Ashok Chandra is a computer scientist known for his contributions to theoretical computer science and complexity theory.
-
C.
Ashok Mishra
Ashok Mishra is an Indian screenwriter known for his work on films such as "Welcome to Sajjanpur."
-
D.
Ashok Dinda
Ashok Dinda is an Indian fast bowler who played domestic cricket for Bengal and represented India in both One Day Internationals and Twenty20 Internationals.
-
E.
Ajit Bhawan
Ajit Bhawan is a historic royal residence in Jodhpur that has been converted into a luxury heritage hotel associated with the Jodhpur royal family.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66847ed9481908e6b23b399fa7005 |
completed | April 20, 2026, 5:54 p.m. |
Created at: April 11, 2026, 11:35 p.m.