Triple
T32144698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peer |
E820994
|
entity |
| Predicate | hasNeighbouringMunicipalities |
P33892
|
FINISHED |
| Object | Bocholt |
—
|
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: Bocholt | Statement: [Peer, hasNeighbouringMunicipalities, Bocholt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeighbouringMunicipalities Context triple: [Peer, hasNeighbouringMunicipalities, Bocholt]
-
A.
neighbouringMunicipality
chosen
Indicates that one municipality directly borders and is adjacent to another municipality.
-
B.
hasNeighbouringAdministrativeUnitType
Indicates that one administrative unit is directly adjacent to another administrative unit of a specified type.
-
C.
isBorderMunicipality
Indicates that a municipality is located on or directly adjacent to the border of a larger administrative region, country, or jurisdiction.
-
D.
hasNeighbouringCanton
Indicates that one canton is geographically adjacent to and shares a common border with another canton.
-
E.
hasNeighbouringDivision
Indicates that one division is directly adjacent to and shares a boundary with another division.
- F. None of above.
Provenance (3 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_69f3490520d081909b2f1271dab75faa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff29d831b881908d485609e0fc1d0b |
completed | May 9, 2026, 12:34 p.m. |
| PD | Predicate disambiguation | batch_69ff28f9f9e4819087f3402735de66c7 |
completed | May 9, 2026, 12:30 p.m. |
Created at: May 1, 2026, 12:31 a.m.