Triple
T17832205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nunkirchen |
E445284
|
entity |
| Predicate | nearbyTown |
P3883
|
FINISHED |
| Object | Weiskirchen |
—
|
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: Weiskirchen | Statement: [Nunkirchen, nearbyTown, Weiskirchen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weiskirchen Context triple: [Nunkirchen, nearbyTown, Weiskirchen]
-
A.
Weiskirchen
chosen
Weiskirchen is a municipality in the Saarland region of western Germany, known for its scenic natural surroundings and spa facilities.
-
B.
Westkirchen
Westkirchen is a village and district of the town of Ennigerloh in North Rhine-Westphalia, Germany, known for its rural character and historic church-centered settlement.
-
C.
Schweitenkirchen
Schweitenkirchen is a municipality in Bavaria, Germany, situated in the district of Pfaffenhofen an der Ilm.
-
D.
Taufkirchen
Taufkirchen is a municipality in Bavaria, Germany, known for its strong aerospace and defense industry presence.
-
E.
Niederkirchen
Niederkirchen is a municipality in western Germany, known for its rural character and cultural ties to its French twin town, Château-Thierry.
- 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_69d8b9f1a6d881909f024bc603111cdb |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48d257414819088730f48ad7ab9ae |
completed | April 19, 2026, 8:07 a.m. |
Created at: April 10, 2026, 10:15 a.m.