Triple
T25709192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kapaklı |
E644681
|
entity |
| Predicate | hasNeighbouringDistrictInProvince |
P168028
|
FINISHED |
| Object | Çerkezköy |
—
|
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: Çerkezköy | Statement: [Kapaklı, hasNeighbouringDistrictInProvince, Çerkezköy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeighbouringDistrictInProvince Context triple: [Kapaklı, hasNeighbouringDistrictInProvince, Çerkezköy]
-
A.
hasNearbyProvince
Indicates that one province is geographically close to or directly adjacent to another province.
-
B.
hasNeighbouringDivision
chosen
Indicates that one division is directly adjacent to and shares a boundary with another division.
-
C.
hasRelatedDistrict
Indicates that one entity is associated with, linked to, or falls within the scope of a particular district.
-
D.
adjacentProvince
Indicates that two provinces share a common boundary and are directly next to each other geographically.
-
E.
hasAdjacentArrondissement
Indicates that one arrondissement is directly next to or shares a boundary with another arrondissement.
- F. None of above.
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_69e77e83c8ec8190bf52fcdac4838984 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6b2a65c7c8190ac40f1466ceadefc |
completed | May 3, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f6b14d7d508190bc7d4c89dfba4a32 |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 21, 2026, 9:09 p.m.