Triple
T2746411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orange (France) |
E60879
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Arausio |
E23934
|
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: Arausio | Statement: [Orange (France), formerName, Arausio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arausio Context triple: [Orange (France), formerName, Arausio]
-
A.
Arausio
chosen
Arausio is the ancient Latin name for the town now known as Orange in southeastern France, historically notable for its Roman monuments and heritage.
-
B.
Combarbalá
Combarbalá is a small Chilean town and municipality in the Coquimbo Region, known for its semi-arid landscapes, goat farming, and distinctive combarbalite stone crafts.
-
C.
Supía
Supía is a municipality in the Caldas Department of Colombia, known historically for gold mining and its indigenous Emberá Chamí heritage.
-
D.
Caxangá
Caxangá is a neighborhood and important urban area within the city of Recife, Brazil.
-
E.
Andalgalá
Andalgalá is a town in northwestern Argentina known for its mining activities and scenic location in the foothills of the Andes within Catamarca Province.
- 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb4ed6bc8190876c1d188b97692b |
completed | March 7, 2026, 8:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbd0f3a08190bf33a937ae9749c7 |
completed | March 10, 2026, 6:36 a.m. |
Created at: March 6, 2026, 9:56 p.m.