Triple
T22789341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hallenberg |
E564064
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | Hallenberg (core town) |
—
|
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: Hallenberg (core town) | Statement: [Hallenberg, hasSubdivision, Hallenberg (core town)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hallenberg (core town) Context triple: [Hallenberg, hasSubdivision, Hallenberg (core town)]
-
A.
Hallenberg
chosen
Hallenberg is a small town and municipality in the Hochsauerland district of North Rhine-Westphalia, Germany, known for its scenic location in the Sauerland region.
-
B.
Kalenberg
Kalenberg is a small waterside village in the Dutch province of Overijssel, known for its canals, reedlands, and traditional houses amid the wetlands of the Weerribben-Wieden area.
-
C.
Kalenberg
Kalenberg is a small district (Ortsteil) of the town of Mechernich in the Euskirchen district of North Rhine-Westphalia, Germany.
-
D.
Langenberg
Langenberg is a prominent mountain in the Rothaargebirge range of Germany, known as the highest peak in the state of North Rhine-Westphalia.
-
E.
Langenhorn
Langenhorn is a residential quarter in the northern part of Hamburg, Germany, known for its green spaces and suburban character.
- 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c3488708190812f7d2edac92184 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 3:29 p.m.