Triple
T20948882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capraia |
E515924
|
entity |
| Predicate | hasHighestPoint |
P210
|
FINISHED |
| Object | Monte Castello |
—
|
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: Monte Castello | Statement: [Capraia, hasHighestPoint, Monte Castello]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Castello Context triple: [Capraia, hasHighestPoint, Monte Castello]
-
A.
Monte Castello
chosen
Monte Castello is a mountain peak in the Alpe di Catenaia range in Italy, notable as its highest summit.
-
B.
Monte Ramaceto
Monte Ramaceto is a mountain in the Ligurian Apennines of northwestern Italy, known for its panoramic views over the Ligurian coast and surrounding valleys.
-
C.
Montelepre
Montelepre is a small historic town in Sicily, Italy, known for its mountainous setting and traditional Sicilian culture.
-
D.
Montecreto
Montecreto is a small municipality in the Emilia-Romagna region of northern Italy, situated in the Apennine Mountains within the Province of Modena.
-
E.
Monte Lungo
Monte Lungo is the highest peak in Italy’s Berici Hills, a low mountain range in the Veneto region.
- 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_69e0b4fcd678819087a304291f14330a |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fadc08148190b4ff710f94462a26 |
completed | April 21, 2026, 4:19 a.m. |
Created at: April 16, 2026, 1:10 p.m.