Triple
T22836109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nikola, count of Sredets |
E565951
|
entity |
| Predicate | positionHeld |
P8
|
FINISHED |
| Object | count of Sredets |
—
|
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: count of Sredets | Statement: [Nikola, count of Sredets, positionHeld, count of Sredets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: count of Sredets Context triple: [Nikola, count of Sredets, positionHeld, count of Sredets]
-
A.
Sredets
chosen
Sredets is a small town and municipal center in southeastern Bulgaria known for its proximity to the city of Burgas and the Strandzha mountain region.
-
B.
Sədərək
Sədərək is a settlement and district in the Nakhchivan Autonomous Republic of Azerbaijan, known for hosting a key border crossing with Turkey.
-
C.
Serdinya
Serdinya is a small commune in the Pyrénées-Orientales department of southern France, situated in the historic region of Conflent in the eastern Pyrenees.
-
D.
Shavsheti
Shavsheti is a historical region or locality within the medieval Georgian cultural-geographical area of Tao-Klarjeti.
-
E.
Serhedi
Serhedi is a regional dialect of Kurmanji Kurdish spoken in parts of the Kurdish-inhabited areas of the Middle East.
- 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e2f09608190bc8e465e53b39e2e |
completed | April 29, 2026, 3:42 a.m. |
Created at: April 17, 2026, 3:35 p.m.