Triple
T18875937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Musante |
E461685
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Toma |
—
|
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: Toma | Statement: [Tony Musante, notableWork, Toma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toma Context triple: [Tony Musante, notableWork, Toma]
-
A.
Toma
Toma is a traditional semi-hard cow’s milk cheese from Italy’s Piedmont region, known for its mild, buttery flavor and smooth, elastic texture.
-
B.
Toma
chosen
Toma is a masculine given name used in various European cultures, often as a form of Thomas.
-
C.
Toma
Toma is a small town located in Kamikawa Subprefecture on Japan’s northern island of Hokkaido.
-
D.
Toma
Toma is a major Mande language spoken primarily in Guinea and neighboring West African countries.
-
E.
Saca
Saca is a Spanish-language surname most notably associated with former Salvadoran president Antonio Saca.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c3ce07788190a179705eb1b6c824 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 10, 2026, 11:57 a.m.