Triple
T2038974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iranian Azerbaijan |
E44697
|
entity |
| Predicate | traditionalDance |
P1114
|
FINISHED |
| Object | Yalli |
E45444
|
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: Yalli | Statement: [Iranian Azerbaijan, traditionalDance, Yalli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yalli Context triple: [Iranian Azerbaijan, traditionalDance, Yalli]
-
A.
Yalli
chosen
Yalli is a traditional Azerbaijani group folk dance characterized by dancers holding hands or shoulders and moving in synchronized circular or linear formations.
-
B.
Lemi
Lemi is a small rural municipality in southeastern Finland known for its lakes, forests, and traditional Karelian culture.
-
C.
Ihnasya
Ihnasya is a city in Egypt known for its location within the Beni Suef Governorate along the Nile Valley.
-
D.
Tessalit
Tessalit is a remote desert town in northern Mali that serves as a key Tuareg cultural center and strategic crossroads in the Adrar des Ifoghas region.
-
E.
Barcha
Barcha is the surname of Mercedes Barcha, the Colombian wife and lifelong companion of Nobel Prize–winning author Gabriel García Márquez.
- 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_69a889159ec481908f9e4472d9f480c7 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb951870481909dbdd8fc8b0c02fe |
completed | March 7, 2026, 5:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1ff63de081908795a95c998dd9ac |
completed | March 9, 2026, 1:18 a.m. |
Created at: March 4, 2026, 7:39 p.m.