Triple
T17670839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pimple Nilakh |
E440515
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Baner |
—
|
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: Baner | Statement: [Pimple Nilakh, locatedNear, Baner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baner Context triple: [Pimple Nilakh, locatedNear, Baner]
-
A.
Baner
chosen
Baner is a rapidly developing residential and commercial suburb in the western part of Pune, Maharashtra, known for its IT offices, eateries, and proximity to major tech hubs.
-
B.
Bangar
Bangar is a coastal municipality in the province of La Union in the Philippines, known for its handwoven textiles and agricultural products.
-
C.
Shingora
Shingora is a film featuring Indian actress and model Persis Khambatta, known for her distinctive screen presence and international appeal.
-
D.
Banshiwala
Banshiwala is a Bengali novel by acclaimed writer Shirshendu Mukhopadhyay, known for its evocative storytelling and exploration of human relationships.
-
E.
Balwa
Balwa is a municipality-level city located in Nepal's Madhesh Province.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f68a038819084828ff73bcf1fcc |
completed | April 19, 2026, 6 a.m. |
Created at: April 10, 2026, 9:59 a.m.