Triple
T22793609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westfalen |
E564180
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Minden |
—
|
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: Minden | Statement: [Westfalen, containsCity, Minden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Minden Context triple: [Westfalen, containsCity, Minden]
-
A.
Minden
chosen
Minden is a historic German city in North Rhine-Westphalia known for its strategic location on the Weser River and its well-preserved old town.
-
B.
Minden
Minden is a small city in northwestern Louisiana known for its historic downtown, antebellum homes, and role as the seat of Webster Parish.
-
C.
Minden
Minden is a small rural town located within the Somerset Region of Queensland, Australia.
-
D.
Minden
Minden is a small community in Ontario, Canada, serving as a local hub for services, commerce, and administration in the surrounding Minden Hills area.
-
E.
Minde
Minde is a civil parish in the municipality of Alcanena in central Portugal, known for its traditional culture and karst landscape.
- 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.