Triple
T22229341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sikandar Kher |
E549425
|
entity |
| Predicate | basedIn |
P40
|
FINISHED |
| Object | Mumbai |
—
|
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: Mumbai | Statement: [Sikandar Kher, basedIn, Mumbai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mumbai Context triple: [Sikandar Kher, basedIn, Mumbai]
-
A.
Mumbai
chosen
Mumbai is a densely populated coastal metropolis in western India that serves as the country’s financial hub and the center of its film industry, Bollywood.
-
B.
Bombay
Bombay is a small rural settlement in the Auckland Region of New Zealand, located just south of the metropolitan area near the Bombay Hills.
-
C.
Mambai
Mambai is an Austronesian language spoken primarily in East Timor, where it is one of the country’s major indigenous languages.
-
D.
Mumbai Metropolitan Region
The Mumbai Metropolitan Region is a vast urban agglomeration in western India centered on Mumbai, encompassing the city and its surrounding suburbs and satellite towns.
-
E.
Pune
Pune is a major cultural, educational, and IT hub in the western Indian state of Maharashtra, known for its universities, historical significance, and rapidly growing urban economy.
- 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_69e11e4102b881909cf47d3768e25c19 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12bf173308190a3d21bfc59b39728 |
completed | April 28, 2026, 9:51 p.m. |
Created at: April 16, 2026, 8:37 p.m.