Triple
T6091922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | In Treatment |
E135784
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Irrfan Khan |
E125488
|
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: Irrfan Khan | Statement: [In Treatment, starring, Irrfan Khan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Irrfan Khan Context triple: [In Treatment, starring, Irrfan Khan]
-
A.
Irrfan Khan
chosen
Irrfan Khan was a critically acclaimed Indian actor known for his powerful performances in both Bollywood and international films such as "The Namesake," "Slumdog Millionaire," and "Life of Pi."
-
B.
Naseeruddin Shah
Naseeruddin Shah is a renowned Indian actor and director celebrated for his powerful performances in parallel cinema as well as mainstream Bollywood films.
-
C.
Sanjeev Bhaskar
Sanjeev Bhaskar is a British comedian, actor, and writer best known for his work on the sketch show "Goodness Gracious Me" and the sitcom "The Kumars at No. 42."
-
D.
Aamir Khan
Aamir Khan is a renowned Indian film actor, director, and producer known for his critically acclaimed and socially impactful movies in Bollywood.
-
E.
Himesh Patel
Himesh Patel is a British actor best known for his breakout lead role in the film "Yesterday" and supporting performances in major productions like "Tenet" and the series "Station Eleven."
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057ab7324819086d4708e6f9391c0 |
completed | March 22, 2026, 8:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c125365a7481909e40d01c2d3590aa |
completed | March 23, 2026, 11:34 a.m. |
Created at: March 22, 2026, 4:12 p.m.