Triple
T9869448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tungabhadra Dam |
E239917
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Hospet |
E182776
|
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: Hospet | Statement: [Tungabhadra Dam, nearCity, Hospet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hospet Context triple: [Tungabhadra Dam, nearCity, Hospet]
-
A.
Hospet
chosen
Hospet is a town in the Vijayanagara district of Karnataka, India, known as a major gateway to the UNESCO World Heritage site of Hampi.
-
B.
Haldia
Haldia is an industrial port city in eastern India known for its petrochemical complexes and role as a major river port on the Hooghly River.
-
C.
Talfer
The Talfer is a river in South Tyrol, northern Italy, that flows through the city of Bolzano as a tributary of the Adige.
-
D.
Sospel
Sospel is a historic village in southeastern France near the Italian border, known for its medieval architecture and picturesque setting in the Maritime Alps.
-
E.
Martos
Martos is a historic town in southern Spain’s Andalusia region, known for its olive oil production and hilltop setting dominated by a medieval castle.
- 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_69ca84e7506c819095cbde4ff16512bb |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3d498b481908f82f31f98b57c7e |
completed | April 2, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e46209988190b97aefee6cbddbad |
completed | April 5, 2026, 4:26 a.m. |
Created at: March 30, 2026, 8:36 p.m.