Triple
T16147130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hasan Minhaj |
E391811
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Beena Patel |
E787868
|
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: Beena Patel | Statement: [Hasan Minhaj, spouse, Beena Patel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beena Patel Context triple: [Hasan Minhaj, spouse, Beena Patel]
-
A.
Beena Patel
chosen
Beena Patel is an American health professional and public health consultant best known as the wife of comedian and political commentator Hasan Minhaj.
-
B.
Gita Patel
Gita Patel is the mother of Pi Patel, the protagonist of Yann Martel’s novel "Life of Pi," and is portrayed as a compassionate and grounding influence in his life.
-
C.
Rashmi Patel
Rashmi Patel is a personal name shared by multiple individuals, typically of Indian origin, and may refer to various professionals or public figures.
-
D.
Neela Rasgotra
Neela Rasgotra is a fictional surgical resident and later attending physician on the medical drama series "ER," known for her intelligence, compassion, and complex personal relationships.
-
E.
Palak Patel
Palak Patel is a film producer known for working on major Hollywood fantasy and action films, including "Snow White and the Huntsman."
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d947e68819081b4b7c757ce71b6 |
completed | April 17, 2026, 11:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7a7dc3481909f933acd72d6feff |
completed | May 10, 2026, 3:12 a.m. |
Created at: April 10, 2026, 5:01 a.m.