Triple
T23358769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke of Camerino |
E593125
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Camerino |
—
|
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: Camerino | Statement: [Duke of Camerino, locatedIn, Camerino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Camerino Context triple: [Duke of Camerino, locatedIn, Camerino]
-
A.
Camerino
chosen
Camerino is a historic hilltop town in Italy’s Marche region, known for its medieval architecture and the University of Camerino.
-
B.
Cascia
Cascia is a historic hill town and pilgrimage site in the Umbria region of central Italy, best known for its association with Saint Rita of Cascia.
-
C.
Montefortino
Montefortino is a small historic town in Italy’s Marche region, known for its scenic Apennine mountain setting and traditional rural character.
-
D.
Loiano
Loiano is a small Italian town in the Emilia-Romagna region, known for its Apennine hillside setting and astronomical observatory.
-
E.
Loretano
Loretano is a regional dialect of the Mojeño language spoken by Indigenous communities in the Bolivian lowlands.
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19a196b308190bfe9bb4b6e7ec363 |
completed | April 29, 2026, 5:41 a.m. |
Created at: April 17, 2026, 5:29 p.m.