Triple
T10228280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Chênaie |
E243267
|
entity |
| Predicate | cantonCode |
P28599
|
FINISHED |
| Object | GE |
E129065
|
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: GE | Statement: [La Chênaie, cantonCode, GE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GE Context triple: [La Chênaie, cantonCode, GE]
-
A.
GE
GE is the ISO 3166-1 alpha-2 country code for Georgia, a nation at the crossroads of Eastern Europe and Western Asia.
-
B.
GE
chosen
GE is the Swiss canton code for Geneva, a major city and canton in western Switzerland known for its international organizations and financial center.
-
C.
GE
GE is the abbreviation for ICANN’s Government Engagement function, which manages and coordinates ICANN’s relationships and interactions with governments and intergovernmental organizations worldwide.
-
D.
GE
GE is the commonly used abbreviation for Global Entry, a U.S. government program that provides expedited clearance for pre-approved, low-risk international travelers entering the United States.
-
E.
GM
GM is the vehicle registration code used on license plates for vehicles registered in the Oberbergischer Kreis district in North Rhine-Westphalia, Germany.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d1fb93688190a9abcbebd9fede6c |
completed | April 7, 2026, 9:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f72bc5388190a8337aa6a60ed51f |
completed | April 9, 2026, 12:47 a.m. |
Created at: April 6, 2026, 11:18 a.m.