Triple
T3842926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jevanshir uezd |
E93495
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Tartar |
E392404
|
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: Tartar | Statement: [Jevanshir uezd, capital, Tartar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tartar Context triple: [Jevanshir uezd, capital, 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.
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.
-
C.
Scheggino
Scheggino is a small historic village in the Umbria region of central Italy, known for its medieval architecture and scenic riverside setting.
-
D.
Tartegnin
Tartegnin is a small wine-producing municipality in the canton of Vaud in western Switzerland, situated above Lake Geneva in the La Côte region.
-
E.
Mezzetin
Mezzetin is a painting by Antoine Watteau depicting a melancholic commedia dell’arte musician in a theatrical, romantic setting.
- 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_69aed96ce578819084ab16e3439976c9 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeebb397ac81908f74a42a0eeb8682 |
completed | March 9, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5122ba6a4819099986e50f42f2a92 |
completed | March 14, 2026, 7:45 a.m. |
Created at: March 9, 2026, 3:18 p.m.