Triple
T17317487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monuments historiques of Bas-Rhin |
E420465
|
entity |
| Predicate | appliesTo |
P1129
|
FINISHED |
| Object | Sélestat |
E57547
|
NE 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: Sélestat | Statement: [Monuments historiques of Bas-Rhin, appliesTo, Sélestat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sélestat Context triple: [Monuments historiques of Bas-Rhin, appliesTo, Sélestat]
-
A.
Sélestat
chosen
Sélestat is a historic town in the Alsace region of northeastern France, known for its well-preserved medieval architecture and cultural heritage.
-
B.
Illzach
Illzach is a commune in northeastern France’s Grand Est region, situated near the city of Mulhouse in the Haut-Rhin department.
-
C.
Wissembourg
Wissembourg is a historic town in northeastern France’s Alsace region, known for its well-preserved medieval architecture and proximity to the German border.
-
D.
Kaysersberg
Kaysersberg is a picturesque medieval town in France’s Alsace region, renowned for its half-timbered houses, hillside vineyards, and well-preserved historic charm.
-
E.
Saint-Witz
Saint-Witz is a small commune in the Val-d'Oise department in northern France, known for its residential character and proximity to Paris and Charles de Gaulle Airport.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4399ea7dc8190a0ecab7534fe16c3 |
completed | April 19, 2026, 2:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180e8cfd88190a105b778e5b9e864 |
completed | May 11, 2026, 7:10 a.m. |
Created at: April 10, 2026, 5:43 a.m.