Triple
T11043232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nera River |
E261071
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Narni |
E212529
|
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: Narni | Statement: [Nera River, flowsThrough, Narni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Narni Context triple: [Nera River, flowsThrough, Narni]
-
A.
Narni
chosen
Narni is a historic hilltop town in the Umbria region of central Italy, known for its medieval architecture and strategic position overlooking the Nera River valley.
-
B.
Crevalcore
Crevalcore is a small Italian town in the Emilia-Romagna region, known for its agricultural surroundings and historic architecture within the Bologna metropolitan area.
-
C.
Nocciano
Nocciano is a small Italian town and comune in the Abruzzo region, known for its historic hilltop setting and traditional rural character.
-
D.
Altamura
Altamura is a historic town in the Apulia region of southern Italy, renowned for its well-preserved medieval center and its traditional DOP-certified Altamura bread.
-
E.
Camerino
Camerino is a historic hilltop town in Italy’s Marche region, known for its medieval architecture and the University of Camerino.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7982d42bc81908ac10f54a7b43fb7 |
completed | April 9, 2026, 12:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a9f180688190ab2d1142b30a2836 |
completed | April 18, 2026, 3:57 p.m. |
Created at: April 8, 2026, 9:26 p.m.