Triple
T19273824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Well Done Abba |
E481994
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Ashok Mishra |
E1384989
|
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: Ashok Mishra | Statement: [Well Done Abba, screenwriter, Ashok Mishra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashok Mishra Context triple: [Well Done Abba, screenwriter, Ashok Mishra]
-
A.
Ashok Mishra
chosen
Ashok Mishra is an Indian screenwriter known for his work on films such as "Welcome to Sajjanpur."
-
B.
Kailashpati Mishra
Kailashpati Mishra was an Indian politician and leader of the Bharatiya Janata Party who served in various governmental and organizational roles.
-
C.
Pramod Kumar Mishra
Pramod Kumar Mishra is a senior Indian Administrative Service officer who serves as a key bureaucratic advisor and top aide to the Prime Minister of India.
-
D.
Vijay Maurya
Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
-
E.
Akhilendra Mishra
Akhilendra Mishra is an Indian film and television actor known for his character roles in Hindi cinema and TV, including notable performances in movies like Lagaan and Sarfarosh.
- 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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fbba7758819081c1c78667c59c5e |
completed | April 20, 2026, 10:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a077ecd79c881908ed825bd89e9f9b8 |
completed | May 15, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:29 p.m.