Triple
T22502950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mulhouse–Colmar railway |
E556320
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Mulhouse |
—
|
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: Mulhouse | Statement: [Mulhouse–Colmar railway, connects, Mulhouse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mulhouse Context triple: [Mulhouse–Colmar railway, connects, Mulhouse]
-
A.
Mulhouse
chosen
Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
-
B.
Strasbourg
Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
-
C.
Illkirch-Graffenstaden
Illkirch-Graffenstaden is a suburban commune in northeastern France, located just south of Strasbourg in the Grand Est region.
-
D.
Altkirch
Altkirch is a small historic town in northeastern France that serves as an administrative and cultural center in the Alsace region.
-
E.
Besançon
Besançon is a historic city in eastern France, known for its well-preserved Vauban fortifications, rich cultural heritage, and role as a regional administrative and educational center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e555edc81909ca803587dafd747 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15d5a01888190ba65a05616b63cbe |
completed | April 29, 2026, 1:22 a.m. |
Created at: April 16, 2026, 8:50 p.m.