Triple
T12643748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chaudfontaine |
E301964
|
entity |
| Predicate | sharesBorderWith |
P224
|
FINISHED |
| Object | Fléron |
E305457
|
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: Fléron | Statement: [Chaudfontaine, sharesBorderWith, Fléron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fléron Context triple: [Chaudfontaine, sharesBorderWith, Fléron]
-
A.
Fléron
chosen
Fléron is a municipality in eastern Belgium located in the Walloon region’s Province of Liège.
-
B.
Mouriès
Mouriès is a village in southern France’s Provence region, known for its olive oil production and location near the Alpilles hills.
-
C.
Vaugier
Vaugier is the surname of Emmanuelle Vaugier, a Canadian actress and model known for roles in television series such as "Two and a Half Men" and "Smallville."
-
D.
Montagnieu
Montagnieu is a commune in the Ain department of eastern France, situated within the historic wine-producing area of Bugey.
-
E.
Malaucène
Malaucène is a picturesque Provençal village in southeastern France, known as a popular base for cyclists and tourists visiting and climbing Mont Ventoux.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9614bf2f881909976becdf747f4fb |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6719a45d881908dd895836a225781 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:17 p.m.