Triple
T12129479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panvel |
E288896
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Navi Mumbai |
E352594
|
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: Navi Mumbai | Statement: [Panvel, partOf, Navi Mumbai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Navi Mumbai Context triple: [Panvel, partOf, Navi Mumbai]
-
A.
Navi Mumbai
chosen
Navi Mumbai is a planned satellite city across the harbor from Mumbai, developed to decongest the main metropolis and featuring organized residential, commercial, and industrial zones.
-
B.
Thane
Thane is a major city in western India known for its numerous lakes and its proximity to Mumbai.
-
C.
Panvel
Panvel is a major railway and commercial hub in Navi Mumbai, Maharashtra, serving as an important junction and gateway between Mumbai and the wider Konkan region.
-
D.
Kalyan-Dombivli
Kalyan-Dombivli is a major twin-city and residential-industrial hub in the Thane district of Maharashtra, forming an important suburban cluster near Mumbai.
-
E.
Andheri
Andheri is a major residential, commercial, and transport hub in Mumbai, India, known for its busy railway station, metro connectivity, and proximity to the city’s airports and film industry areas.
- 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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9158a2c2c8190aaff9d0cce177565 |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbac5b0881908cc98458f1b3004d |
completed | May 3, 2026, 4:14 a.m. |
Created at: April 8, 2026, 9:49 p.m.