Triple
T18896558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dhirubhai Ambani |
E462223
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Deepti Salgaonkar |
—
|
NE NERFINISHED |
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: Deepti Salgaonkar | Statement: [Dhirubhai Ambani, child, Deepti Salgaonkar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deepti Salgaonkar Context triple: [Dhirubhai Ambani, child, Deepti Salgaonkar]
-
A.
Deepti Salgaonkar
chosen
Deepti Salgaonkar is a member of the prominent Ambani family, known as a daughter of Indian business matriarch Kokilaben Ambani.
-
B.
Sonali Kulkarni
Sonali Kulkarni is an acclaimed Indian actress known for her versatile performances across Marathi and Hindi cinema, as well as in international films.
-
C.
Kavita Rao
Kavita Rao is a fictional geneticist in the X-Men universe known for developing a controversial "cure" for mutant powers.
-
D.
Divya Katdare
Divya Katdare is a central character on the television series "Royal Pains," known as a skilled and poised physician assistant who works closely with concierge doctor Hank Lawson in the Hamptons.
-
E.
Devi Parikh
Devi Parikh is a computer vision and AI researcher known for her work on visual question answering, human-AI collaboration, and interpretable machine learning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c47f6c948190918ade08bb88f1fd |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 10, 2026, 11:58 a.m.