Triple
T5524577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dimple Kapadia |
E144890
|
entity |
| Predicate | motherInLawOf |
P18075
|
FINISHED |
| Object | Akshay Kumar |
E528625
|
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: Akshay Kumar | Statement: [Dimple Kapadia, motherInLawOf, Akshay Kumar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akshay Kumar Context triple: [Dimple Kapadia, motherInLawOf, Akshay Kumar]
-
A.
Akshay Kumar
chosen
Akshay Kumar is a prominent Indian film actor and producer, known for his action and comedy roles in Bollywood and his long-running, commercially successful career.
-
B.
Akshaye Khanna
Akshaye Khanna is an Indian film actor known for his versatile performances in Hindi cinema across both commercial hits and critically acclaimed dramas.
-
C.
Aamir Khan
Aamir Khan is a renowned Indian film actor, director, and producer known for his critically acclaimed and socially impactful movies in Bollywood.
-
D.
Salman Khan
Salman Khan is an American educator and entrepreneur best known as the founder of the online learning platform Khan Academy.
-
E.
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.
- 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_69c008f873a481909b4d9f7e2db3c37d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f874bd081909cccfc25767ee6fa |
completed | March 22, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04cd6ce3c8190ac5ef4c216190266 |
completed | March 22, 2026, 8:11 p.m. |
Created at: March 22, 2026, 3:34 p.m.