Triple
T11414911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | arrondissement of Haguenau-Wissembourg |
E270464
|
entity |
| Predicate | hasCommunesNear |
P99183
|
FINISHED |
| Object | German border |
—
|
LITERAL FINISHED |
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: German border | Statement: [arrondissement of Haguenau-Wissembourg, hasCommunesNear, German border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommunesNear Context triple: [arrondissement of Haguenau-Wissembourg, hasCommunesNear, German border]
-
A.
hasCommune
Indicates a relationship where an entity is associated with, belongs to, or is located within a specific commune (municipal administrative unit).
-
B.
hasNeighboringFrenchCommune
Indicates that one commune is geographically adjacent to another commune located in France.
-
C.
hasRuralCommunes
Indicates that an entity possesses, includes, or is associated with one or more rural communes.
-
D.
hasNearbyProvince
Indicates that one province is geographically close to or directly adjacent to another province.
-
E.
hasNearbyTown
Indicates that one location has a town situated close to it in geographic proximity.
- F. None of above. chosen
Provenance (4 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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d801ae47d0819098123505309c4a68 |
completed | April 9, 2026, 7:44 p.m. |
| PD | Predicate disambiguation | batch_69d7e70ffd708190b62a78ebcbce9f78 |
completed | April 9, 2026, 5:51 p.m. |
| PDg | Predicate description generation | batch_69d80010712c819089ea2e31e664abe1 |
completed | April 9, 2026, 7:37 p.m. |
Created at: April 8, 2026, 9:34 p.m.