Triple
T7784763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bhagalpur |
E187213
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bihar |
E60958
|
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: Bihar | Statement: [Bhagalpur, locatedIn, Bihar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bihar Context triple: [Bhagalpur, locatedIn, Bihar]
-
A.
Bihar
chosen
Bihar is a populous state in eastern India known for its rich historical heritage, including ancient centers of learning like Nalanda and significant sites in Buddhist history.
-
B.
Jharkhand
Jharkhand is an eastern Indian state known for its rich mineral resources, significant tribal population, and extensive forests and plateaus.
-
C.
Madhya Pradesh
Madhya Pradesh is a large central Indian state known for its historical cities, diverse tribal cultures, and significant forested and wildlife areas including several major national parks.
-
D.
Chhattisgarh
Chhattisgarh is a state in central India known for its rich mineral resources, dense forests, tribal cultures, and growing industrial and power sectors.
-
E.
West Bengal
West Bengal is an eastern Indian state known for its cultural heritage, literature, and the metropolis of Kolkata (formerly Calcutta).
- 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_69ca82af2d2c8190963861f5e0b8bf21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cadf210f508190b215a0ab95192689 |
completed | March 30, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69caf5f4fa6c8190a85ba9019c0e5345 |
completed | March 30, 2026, 10:15 p.m. |
Created at: March 30, 2026, 4:23 p.m.