Triple
T4283648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower Burgundy |
E97213
|
entity |
| Predicate | significantRegion |
P285
|
FINISHED |
| Object | Vivarais |
E55089
|
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: Vivarais | Statement: [Lower Burgundy, significantRegion, Vivarais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vivarais Context triple: [Lower Burgundy, significantRegion, Vivarais]
-
A.
Vivarais
chosen
Vivarais is a historical region in south-central France, known for its rugged landscapes, part of the broader Massif Central, and its traditional rural culture.
-
B.
Vrilissia
Vrilissia is a suburban municipality in the northeastern part of the Athens metropolitan area in Greece.
-
C.
Vassa
Vassa is the traditional Buddhist rainy-season retreat during which monks remain in one place for intensive meditation and study.
-
D.
Vimioso
Vimioso is a municipality in northeastern Portugal known for its strong cultural ties to the Mirandese language and traditional rural heritage.
-
E.
Verdú
Verdú is a Spanish surname most notably borne by acclaimed film and television actress Maribel Verdú.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3503c062c81908f9a9eeab5381ec9 |
completed | March 12, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c72804ac81908f8d2c111276b6b7 |
completed | March 14, 2026, 8:38 p.m. |
Created at: March 12, 2026, 11:07 p.m.