Triple
T7129667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monferrato |
E166153
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Moncalvo |
E467473
|
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: Moncalvo | Statement: [Monferrato, contains, Moncalvo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moncalvo Context triple: [Monferrato, contains, Moncalvo]
-
A.
Moncalvo
chosen
Moncalvo is a small historic town in Italy’s Piedmont region, known as one of the country’s smallest cities and for its wine and truffle production.
-
B.
Montalva
Montalva is a Spanish-language surname notably associated with Chilean president Eduardo Frei Montalva.
-
C.
Negrete
Negrete is a small town and commune in Chile’s Biobío Region, known for its rural character and location near the Biobío River.
-
D.
Calvero
Calvero is the aging, once-famous clown portrayed by Charlie Chaplin in the 1952 film "Limelight," struggling with obscurity and seeking redemption through helping a young dancer.
-
E.
Almagro
Almagro is a Spanish surname borne by various notable figures, including politicians, athletes, and artists from Spanish-speaking countries.
- 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_69c6888350588190870cd552b427a1cd |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e66c87848190b0ffd08e3c3f4877 |
completed | March 27, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b8e0a06c819091b47dd41acb47b7 |
completed | March 28, 2026, 11:17 a.m. |
Created at: March 27, 2026, 2:44 p.m.