Triple
T23386745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gemen Castle |
E593903
|
entity |
| Predicate | isLocatedNear |
P350
|
FINISHED |
| Object | Borken town centre |
—
|
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: Borken town centre | Statement: [Gemen Castle, isLocatedNear, Borken town centre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Borken town centre Context triple: [Gemen Castle, isLocatedNear, Borken town centre]
-
A.
Borken
chosen
Borken is a town in western Germany that serves as an administrative and commercial center in the state of North Rhine-Westphalia.
-
B.
Bonn pedestrian shopping zone
The Bonn pedestrian shopping zone is a central car-free area in Bonn known for its dense concentration of shops, cafés, and street life.
-
C.
Stadtmitte
Stadtmitte is the central urban district and main downtown area of the town of Eberswalde in Germany.
-
D.
Stadtmitte
Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
-
E.
Stadtmitte
Stadtmitte is the central urban district of the town of Bad Honnef in North Rhine-Westphalia, Germany.
- 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_69e25d2754fc819085deea939bde60ab |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a498fd08819085e90a872d9d0c7a |
completed | April 29, 2026, 6:26 a.m. |
Created at: April 17, 2026, 5:35 p.m.