Triple
T22793607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westfalen |
E564180
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Gütersloh |
—
|
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: Gütersloh | Statement: [Westfalen, containsCity, Gütersloh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gütersloh Context triple: [Westfalen, containsCity, Gütersloh]
-
A.
Gütersloh
chosen
Gütersloh is a city in the German state of North Rhine-Westphalia known for being the headquarters of major companies like Bertelsmann and Miele.
-
B.
Bielefeld
Bielefeld is a major city in northwestern Germany known for its industrial heritage, university, and the tongue-in-cheek “Bielefeld conspiracy” meme claiming it does not exist.
-
C.
Nordhorn
Nordhorn is a town in Lower Saxony, Germany, known as the administrative center of the Grafschaft Bentheim district near the Dutch border.
-
D.
Diepholz
Diepholz is a town in Lower Saxony, Germany, known as a local administrative center and for its surrounding lake district and agricultural landscape.
-
E.
Lippstadt
Lippstadt is a historic town in North Rhine-Westphalia, Germany, known for its medieval architecture and role in regional conflicts.
- 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_69e2458185f88190b0045227ee420411 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17cd751f081909c7907c96c9906ea |
completed | April 29, 2026, 3:36 a.m. |
Created at: April 17, 2026, 3:30 p.m.