Triple
T16589007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naseeruddin Shah |
E403033
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Naseeruddin Shah |
E403033
|
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: Naseeruddin Shah | Statement: [Naseeruddin Shah, birthName, Naseeruddin Shah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naseeruddin Shah Context triple: [Naseeruddin Shah, birthName, Naseeruddin Shah]
-
A.
Naseeruddin Shah
chosen
Naseeruddin Shah is a renowned Indian actor and director celebrated for his powerful performances in parallel cinema as well as mainstream Bollywood films.
-
B.
Kamal Hasan
Kamal Hasan is a renowned Indian film actor, director, and producer celebrated for his versatile performances across multiple Indian film industries, particularly Tamil cinema.
-
C.
Sanjay Sen
Sanjay Sen is known primarily as the husband of acclaimed Indian filmmaker and actress Aparna Sen.
-
D.
Anupam Kher
Anupam Kher is an acclaimed Indian actor known for his extensive work in Hindi cinema and notable roles in international films.
-
E.
Dilip Kumar
Dilip Kumar was a legendary Indian film actor, celebrated as the "Tragedy King" of Hindi cinema and renowned for his intense, nuanced performances in classic Bollywood films.
- 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3599e79288190b6bcdb6fe4a2d1fa |
completed | April 18, 2026, 10:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007da6d6f08190a8524b1c955b7c2e |
completed | May 10, 2026, 12:44 p.m. |
Created at: April 10, 2026, 5:16 a.m.