Triple
T21456178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Azerbaijan |
E529344
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Tartar |
—
|
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: Tartar | Statement: [Central Azerbaijan, hasCity, Tartar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tartar Context triple: [Central Azerbaijan, hasCity, Tartar]
-
A.
Tartar
chosen
Tartar is a town in western Azerbaijan that serves as the administrative and economic hub of the surrounding Tartar District.
-
B.
Tarter
Tarter is the surname of Jill Tarter, an American astronomer renowned for her pioneering work in the search for extraterrestrial intelligence (SETI).
-
C.
Tarro
Tarro is a suburban railway station in the Hunter Region of New South Wales, Australia, serving the local community on the Main Northern line.
-
D.
Sardent
Sardent is a small rural commune in central France, best known as the village setting of Claude Chabrol’s film *Le Beau Serge*.
-
E.
Tarama
Tarama is a small island municipality in Okinawa Prefecture, Japan, known for its subtropical climate, traditional Ryukyuan culture, and surrounding coral reefs.
- 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_69e0c458133481908ae8b41a12c4edec |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d6da3c819082a495f5434c5554 |
completed | April 23, 2026, 9:43 a.m. |
Created at: April 16, 2026, 6:08 p.m.