Triple
T2474965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanded railway station |
E55065
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Nanded |
E55065
|
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: Nanded | Statement: [Nanded railway station, locatedIn, Nanded]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanded Context triple: [Nanded railway station, locatedIn, Nanded]
-
A.
Nanded
chosen
Nanded is a historic city in the Indian state of Maharashtra, known as an important Sikh pilgrimage center and a major urban hub in the Marathwada region.
-
B.
Latur
Latur is a city in the Marathwada region of western India known for its agricultural economy and for being the epicenter of a devastating earthquake in 1993.
-
C.
Gondia
Gondia is a district in the Indian state of Maharashtra, known for its rice production and proximity to forests and wildlife reserves.
-
D.
Sangli
Sangli is a city in the Indian state of Maharashtra known for its fertile agricultural surroundings and prominence in sugar and turmeric production.
-
E.
Chandrapur
Chandrapur is a district in the eastern part of Maharashtra, India, known for its coal mining industry, forests, and wildlife, including the Tadoba-Andhari Tiger Reserve.
- 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_69ab49e279e88190ab10d7248aea9d11 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd14b725c81908256c4d3bb6c5173 |
completed | March 7, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af654726988190a7166434ecf0582c |
completed | March 10, 2026, 12:26 a.m. |
Created at: March 6, 2026, 9:45 p.m.