Triple
T18555518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arausio |
E453492
|
entity |
| Predicate | etymologicalRelation |
P2530
|
FINISHED |
| Object | Orange (city) |
—
|
NE NERFINISHED |
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: Orange (city) | Statement: [Arausio, etymologicalRelation, Orange (city)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orange (city) Context triple: [Arausio, etymologicalRelation, Orange (city)]
-
A.
Orange (city)
chosen
Orange is a historic town in southeastern France, renowned for its well-preserved Roman monuments including a UNESCO-listed ancient theatre and triumphal arch.
-
B.
Orland
Orland is a small agricultural city in Northern California known for its farming community and rural character.
-
C.
Orlando
Orlando is a common Italian surname borne by numerous individuals, including notable political and cultural figures.
-
D.
Orlando
Orlando is the Italian literary counterpart of the medieval knight Roland, best known as the chivalric hero of epic poems such as "Orlando Furioso."
-
E.
Orlando
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53806046481908efbbe6909b2c68b |
completed | April 19, 2026, 8:16 p.m. |
Created at: April 10, 2026, 11:38 a.m.