Triple
T5151127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southwestern Bulgaria |
E116194
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Bansko |
E284306
|
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: Bansko | Statement: [Southwestern Bulgaria, containsCity, Bansko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bansko Context triple: [Southwestern Bulgaria, containsCity, Bansko]
-
A.
Bansko
chosen
Bansko is a Bulgarian mountain town renowned as one of Eastern Europe’s leading ski and winter sports resorts.
-
B.
Smolyan
Smolyan is a Bulgarian town known as an administrative, cultural, and tourist center in the Rhodope Mountains.
-
C.
Asenovgrad
Asenovgrad is a town in southern Bulgaria known as a gateway to the Rhodope Mountains and a regional center rich in historical and religious landmarks.
-
D.
Kazanlak
Kazanlak is a town in central Bulgaria known for its rich Thracian heritage and rose oil production in the Valley of the Roses.
-
E.
Gabrovo
Gabrovo is a town in central Bulgaria known for its humor and satire traditions, as well as its historical role in the country’s industrial development.
- 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_69bd445d94788190b72e2cc563120995 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd78d965548190b09f574acf3b9b1a |
completed | March 20, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bee07283c08190a8fc23d3041275ee |
completed | March 21, 2026, 6:16 p.m. |
Created at: March 20, 2026, 1:44 p.m.