Triple
T23058811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fucino plain |
E574237
|
entity |
| Predicate | nearbyTown |
P3883
|
FINISHED |
| Object | Celano |
—
|
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: Celano | Statement: [Fucino plain, nearbyTown, Celano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Celano Context triple: [Fucino plain, nearbyTown, Celano]
-
A.
Celano
chosen
Celano is a historic town in Italy’s Abruzzo region, known for its medieval Piccolomini Castle overlooking the Fucino plain.
-
B.
Ceccano
Ceccano is a historic town and comune in the Lazio region of central Italy, situated in the Province of Frosinone along the Sacco River.
-
C.
Putignano
Putignano is a historic town in southern Italy’s Apulia region, best known for hosting one of Europe’s oldest and longest-running Carnival celebrations.
-
D.
Maddaloni
Maddaloni is a historic town and municipality in southern Italy’s Campania region, known for its medieval castle and proximity to the city of Caserta.
-
E.
Avezzano
Avezzano is a central Italian city in the Abruzzo region, known as an important agricultural and commercial hub in the Fucino plain.
- 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_69e245ba7ae48190be606dbc54120e39 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1899e6c788190a2a862122cae8dc3 |
completed | April 29, 2026, 4:31 a.m. |
Created at: April 17, 2026, 3:55 p.m.