Triple
T33732485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Münster im Elsass |
E864308
|
entity |
| Predicate | hatDépartement |
P153959
|
FINISHED |
| Object | Haut-Rhin |
—
|
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: Haut-Rhin | Statement: [Münster im Elsass, hatDépartement, Haut-Rhin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hatDépartement Context triple: [Münster im Elsass, hatDépartement, Haut-Rhin]
-
A.
mainFrenchBorderDepartments
Indicates that the referenced departments are the primary administrative regions of France that share a land border with neighboring countries.
-
B.
prefectureOfDepartment
Indicates that a given prefecture administers or is the capital authority of a specified department.
-
C.
cityLocatedInDepartment
Indicates that a city is geographically and administratively situated within a specific department.
-
D.
neighboringFrenchDepartments
Indicates that two French administrative departments share a common border and are geographically adjacent to each other.
-
E.
situéeDansLeDépartement
chosen
Indicates that one entity is located within the administrative boundaries of a specific department.
- 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_69f3498a64cc8190b4b414c67b280d93 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fb1fcda08190a503098914ba09ab |
completed | May 3, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69f6f96dd4c8819093d6a7bd046a9ad5 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:44 a.m.