Triple
T23357679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annemasse station |
E593093
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Annemasse |
—
|
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: Annemasse | Statement: [Annemasse station, serves, Annemasse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annemasse Context triple: [Annemasse station, serves, Annemasse]
-
A.
Annemasse
chosen
Annemasse is a French town in the Haute-Savoie department near the Swiss border, functioning as a key commuter suburb of Geneva and a regional transport hub.
-
B.
Obernai
Obernai is a historic Alsatian town in northeastern France known for its well-preserved medieval architecture, wine production, and picturesque setting along the Alsace Wine Route.
-
C.
Fallières
Fallières is a French surname most notably borne by Armand Fallières, who served as President of France in the early 20th century.
-
D.
Brumath
Brumath is a small commune in northeastern France’s Grand Est region, known for its historical roots dating back to Roman times.
-
E.
Sarreguemines
Sarreguemines is a town in northeastern France near the German border, historically known for its ceramics and faience production.
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19a18996c81909c7ad15cde616553 |
completed | April 29, 2026, 5:41 a.m. |
Created at: April 17, 2026, 5:28 p.m.