Triple
T7356347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diu district |
E169634
|
entity |
| Predicate | administrativeHeadquarters |
P62
|
FINISHED |
| Object | Diu |
E169638
|
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: Diu | Statement: [Diu district, administrativeHeadquarters, Diu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diu Context triple: [Diu district, administrativeHeadquarters, Diu]
-
A.
Diu
chosen
Diu is a coastal town and former Portuguese colonial enclave on India’s western coast, known for its historic fort, churches, and beaches.
-
B.
Dorla
Dorla are an indigenous Adivasi community of the Bastar region in central India, known for their distinct cultural traditions, language, and close relationship with forest-based livelihoods.
-
C.
Daulida
Daulida is an alternative name for the ancient Greek town of Daulis in the region of Phocis.
-
D.
Faeto
Faeto is a small town in southern Italy known for its unique linguistic heritage, including the rare Franco-Provençal dialect Faetar.
-
E.
Diois
Diois is a mountainous, wine-producing region in southeastern France known for its scenic landscapes and sparkling Clairette de Die wine.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f13a62e48190a2d1781a630aa9f0 |
completed | March 27, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802b69cb4819096815b1fac284840 |
completed | March 28, 2026, 4:32 p.m. |
Created at: March 27, 2026, 3:05 p.m.