Triple
T17526193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoornaar |
E426800
|
entity |
| Predicate | isLocatedNear |
P350
|
FINISHED |
| Object | Giessenburg |
—
|
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: Giessenburg | Statement: [Hoornaar, isLocatedNear, Giessenburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Giessenburg Context triple: [Hoornaar, isLocatedNear, Giessenburg]
-
A.
Giessenburg
chosen
Giessenburg is a village in the Dutch province of South Holland, known for its rural character and location along the river Giessen.
-
B.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
-
C.
Giesen
Giesen is a municipality in Lower Saxony, Germany, located within the Hildesheim district.
-
D.
Burggrafenburg
Burggrafenburg is a historic German castle traditionally associated with a burgrave, a medieval noble responsible for the defense and administration of a fortified town or region.
-
E.
Herrenberg
Herrenberg is a historic town in the German state of Baden-Württemberg, known for its well-preserved medieval center and proximity to the Schönbuch Nature Park.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d6a2548190acf26f2d5d4aab66 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.