Triple
T16280061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kula |
E395239
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Crvenka |
E1213998
|
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: Crvenka | Statement: [Kula, hasSettlement, Crvenka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Crvenka Context triple: [Kula, hasSettlement, Crvenka]
-
A.
Crvenka
chosen
Crvenka is a small town in the Vojvodina region of northern Serbia, known historically for its food industry and sugar refinery.
-
B.
Crna Trava
Crna Trava is a small mountainous municipality in southeastern Serbia, historically known for its skilled builders and significant emigration.
-
C.
Krasna
Krasna is a surname most notably associated with American screenwriter, playwright, and film producer Norman Krasna.
-
D.
Vranje
Vranje is a historic city in southern Serbia known for its Ottoman-era architecture, cultural heritage, and role as an administrative and economic center of the region.
-
E.
Vukovica
Vukovica is the standardized orthography of the Serbo-Croatian language based on the phonemic spelling principles codified by linguist Vuk Karadžić.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24611926c81909b276ca3f406f15d |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f3d8f188190969b75d82c6b13f0 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:05 a.m.