Triple
T16125703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Épinal |
E391263
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Gembloux |
E747984
|
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: Gembloux | Statement: [Épinal, hasTwinTown, Gembloux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gembloux Context triple: [Épinal, hasTwinTown, Gembloux]
-
A.
Gembloux
chosen
Gembloux is a town in the Walloon region of Belgium known for its historic center and agricultural university.
-
B.
Braine-le-Comte
Braine-le-Comte is a municipality in the province of Hainaut in Wallonia, Belgium, known for its historic town center and role as a regional transport hub.
-
C.
Fernelmont
Fernelmont is a rural municipality in the province of Namur in Wallonia, Belgium, known for its agricultural landscape and small villages.
-
D.
Kevelaer
Kevelaer is a renowned German pilgrimage town in North Rhine-Westphalia, famous as one of Europe’s most important Marian shrines and a major destination for Catholic pilgrims.
-
E.
Berlencourt
Berlencourt is a small commune in northern France, located within the Nord department in the Hauts-de-France region.
- 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e20204fb408190b58d49d0d64bb740 |
completed | April 17, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fffeef277481908ab35bbff06c827a |
completed | May 10, 2026, 3:43 a.m. |
Created at: April 10, 2026, 5 a.m.