Triple
T22924178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chhatarpur railway station |
E569251
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Chhatarpur |
—
|
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: Chhatarpur | Statement: [Chhatarpur railway station, serves, Chhatarpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chhatarpur Context triple: [Chhatarpur railway station, serves, Chhatarpur]
-
A.
Chhatarpur
chosen
Chhatarpur is a city in central India known as an administrative and commercial center in the Bundelkhand region of Madhya Pradesh.
-
B.
Pithoragarh
Pithoragarh is a town and district in the eastern Kumaon region of Uttarakhand, India, known for its scenic Himalayan landscapes and strategic location near the Nepal and Tibet borders.
-
C.
Narayanpur
Narayanpur is a town located in the Lakhimpur district of the Indian state of Assam.
-
D.
Karauli
Karauli is a historic town and pilgrimage center in the Indian state of Rajasthan, known for its ancient temples and distinctive red sandstone architecture.
-
E.
Ghatshila
Ghatshila is a scenic town in Jharkhand, India, known for its forested hills, waterfalls, and literary association with Bengali writer Bibhutibhushan Bandyopadhyay.
- 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f180d7973c8190b09a5690fd1d3f28 |
completed | April 29, 2026, 3:53 a.m. |
Created at: April 17, 2026, 3:43 p.m.