Triple
T22302486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuljapur |
E551290
|
entity |
| Predicate | nearestMajorCity |
P1982
|
FINISHED |
| Object | Osmanabad |
—
|
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: Osmanabad | Statement: [Tuljapur, nearestMajorCity, Osmanabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osmanabad Context triple: [Tuljapur, nearestMajorCity, Osmanabad]
-
A.
Osmanabad
chosen
Osmanabad is a city in the Indian state of Maharashtra, known as an important urban and administrative center in the Marathwada region.
-
B.
Yaseenabad
Yaseenabad is a residential neighborhood located within the Federal B Area of Karachi, Pakistan.
-
C.
Khairabad
Khairabad is a town in northern India historically known as a center of Islamic scholarship and Urdu and Persian literary culture.
-
D.
Shahpura
Shahpura is a town in Rajasthan, India, historically known as the administrative and cultural center of the former princely Shahpura State.
-
E.
Shikohabad
Shikohabad is a city in the Indian state of Uttar Pradesh, known for its location along major road and rail routes and its role as a regional commercial center.
- 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1572517588190bef6f8bfb76afb19 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.