Triple
T95059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meyrin |
E1912
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Romandy |
E2947
|
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: Romandy | Statement: [Meyrin, locatedIn, Romandy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Romandy Context triple: [Meyrin, locatedIn, Romandy]
-
A.
Romandy
chosen
Romandy is the French-speaking western region of Switzerland, encompassing cantons such as Geneva, Vaud, Neuchâtel, and Jura.
-
B.
Luxembourgish
Luxembourgish is a West Germanic language spoken primarily in Luxembourg, where it serves as a national and administrative language alongside French and German.
-
C.
canton of Geneva
The canton of Geneva is the westernmost Swiss canton, encompassing the city of Geneva and serving as a major international, diplomatic, and financial hub.
-
D.
Switzerland
Switzerland is a landlocked Central European country known for its long-standing neutrality, mountainous landscapes, financial centers, and multilingual, federal political system.
-
E.
Burgundy
Burgundy is a renowned wine-producing region in eastern France, famous for its high-quality Chardonnay and Pinot Noir wines.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24fd4777c81909ea9b9a6bd4f7ad5 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a266ebb994819085fb84dd1d2d25ad |
completed | Feb. 28, 2026, 3:54 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.