Triple
T22157325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tulja Bhavani Temple |
E547573
|
entity |
| Predicate | town |
P3385
|
FINISHED |
| Object | Tuljapur |
—
|
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: Tuljapur | Statement: [Tulja Bhavani Temple, town, Tuljapur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tuljapur Context triple: [Tulja Bhavani Temple, town, Tuljapur]
-
A.
Tuljapur
chosen
Tuljapur is a town in Maharashtra, India, renowned as a major pilgrimage center for the goddess Bhavani.
-
B.
Bhanpura
Bhanpura is a town in the Malwa region of Madhya Pradesh, India, known for its archaeological sites, ancient rock-cut caves, and nearby historical monuments.
-
C.
Dantapura
Dantapura was an ancient city traditionally identified as the royal and administrative center of the Kalinga kingdom in eastern India.
-
D.
Tekanpur
Tekanpur is a town in Madhya Pradesh, India, best known for hosting the Border Security Force’s main training academy.
-
E.
Jagdishpur
Jagdishpur is a town in the Bhojpur district of Bihar, India, historically known as the ancestral estate of the 19th-century freedom fighter Kunwar Singh.
- 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_69e11e3b52088190ad5df386d01eb2fb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12a2aadb48190b4d739d1df9529db |
completed | April 28, 2026, 9:44 p.m. |
Created at: April 16, 2026, 8:33 p.m.