Triple
T22075729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamini |
E545516
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | Mahal |
—
|
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: Mahal | Statement: [Kamini, appearsIn, Mahal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mahal Context triple: [Kamini, appearsIn, Mahal]
-
A.
Mahal
chosen
Mahal is a landmark 1949 Indian Hindi-language psychological horror film, celebrated for pioneering the Bollywood gothic romance genre and launching Madhubala to stardom.
-
B.
Mahal
Mahal is a historic neighborhood in Nagpur, India, known as one of the city’s oldest and most culturally significant localities.
-
C.
Mahal
Mahal is a royal title historically used in the Mughal Empire to denote a queen or high-ranking consort in the imperial harem.
-
D.
Mahal Khas
Mahal Khas is a prominent royal palace structure within Lohagarh Fort, known for its historic architecture and association with the rulers of Bharatpur in Rajasthan, India.
-
E.
Mariakani
Mariakani is a town in Kenya’s coastal region that serves as a key transport and trading hub along the Mombasa–Nairobi corridor.
- 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_69e11e344dfc81909b1d88a7221329c7 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128b1904881909a1769ce8be39e05 |
completed | April 28, 2026, 9:37 p.m. |
Created at: April 16, 2026, 8:28 p.m.