Triple
T10390702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Gray Man |
E244884
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Dhanush |
E207452
|
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: Dhanush | Statement: [The Gray Man, starring, Dhanush]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dhanush Context triple: [The Gray Man, starring, Dhanush]
-
A.
Dhanush
chosen
Dhanush is an acclaimed Indian actor, producer, director, and playback singer best known for his versatile performances in Tamil cinema and his breakout international hit song "Why This Kolaveri Di."
-
B.
Naveen Vikram
Naveen Vikram is an actor known for his role in the acclaimed Tamil film "Super Deluxe."
-
C.
Insp. Dhanush
Insp. Dhanush is a film featuring Indian actress and former Miss India Persis Khambatta in a prominent role.
-
D.
Vijay
Vijay is a leading Indian film actor and playback singer, predominantly known for his work in Tamil cinema and his massive fan following across South India.
-
E.
Allu Arjun
Allu Arjun is a popular Indian film actor renowned for his charismatic performances and exceptional dancing skills in Telugu cinema.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b4f7d08190bcb16d3b4c8f22ad |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d795b9974c819087340adc3622279e |
completed | April 9, 2026, 12:04 p.m. |
Created at: April 6, 2026, 12:06 p.m.