Triple
T22121716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rae Bareli (Lok Sabha constituency) |
E546686
|
entity |
| Predicate | representedBy |
P1748
|
FINISHED |
| Object | Ashok Kumar Singh |
—
|
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 Singh | Statement: [Rae Bareli (Lok Sabha constituency), representedBy, Ashok Kumar Singh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashok Kumar Singh Context triple: [Rae Bareli (Lok Sabha constituency), representedBy, Ashok Kumar Singh]
-
A.
Ashok Mishra
Ashok Mishra is an Indian screenwriter known for his work on films such as "Welcome to Sajjanpur."
-
B.
Ashok Singh
chosen
Ashok Singh is an Indian politician who has served as a Member of Parliament representing the Rae Bareli Lok Sabha constituency.
-
C.
Ashok Kumar
Ashok Kumar was a pioneering and acclaimed Indian film actor, often regarded as one of the first superstars of Hindi cinema.
-
D.
Ashok Chandra
Ashok Chandra is a computer scientist known for his contributions to theoretical computer science and complexity theory.
-
E.
Manoj Kumar Singh
Manoj Kumar Singh is an Indian politician who has served as a Member of Parliament representing the Rae Bareli Lok Sabha constituency.
- 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_69e11e38b3848190ac3a4fa97d56e65a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1297e7e188190873924403421caa2 |
completed | April 28, 2026, 9:41 p.m. |
Created at: April 16, 2026, 8:31 p.m.