Triple
T16685766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pimpri-Chinchwad |
E405457
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Chakan |
E1223191
|
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: Chakan | Statement: [Pimpri-Chinchwad, hasNeighbour, Chakan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chakan Context triple: [Pimpri-Chinchwad, hasNeighbour, Chakan]
-
A.
Chakan
chosen
Chakan is a rapidly developing industrial town near Pune in Maharashtra, India, known for its large automobile and manufacturing hubs.
-
B.
Chacala
Chacala is a small coastal village and beach destination on Mexico’s Pacific coast in the state of Nayarit, known for its tranquil atmosphere, surfing, and ecotourism.
-
C.
Chanac
Chanac is a small commune in the Lozère department of southern France, known for its rural setting in the Massif Central and traditional Occitan character.
-
D.
Chicamán
Chicamán is a rural municipality in Guatemala known for its mountainous terrain, indigenous Maya communities, and traditional agricultural economy.
-
E.
Cajamar
Cajamar is a municipality in the state of São Paulo, Brazil, known for its strategic location within the São Paulo metropolitan region and its growing industrial and logistics sectors.
- 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_69d8838c28748190b3f5967c743940ab |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37ea550c0819085bd36c44237a61a |
completed | April 18, 2026, 12:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a43f6a08190913ca123a2377f95 |
completed | May 10, 2026, 1:38 p.m. |
Created at: April 10, 2026, 5:19 a.m.