Triple
T22469067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ugo Mola |
E555440
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Mola |
—
|
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: Mola | Statement: [Ugo Mola, familyName, Mola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mola Context triple: [Ugo Mola, familyName, Mola]
-
A.
Mola
chosen
Mola is a Spanish surname most notably associated with Emilio Mola, a key Nationalist general during the Spanish Civil War.
-
B.
Mola
Mola is a genus of large ocean sunfishes known for their distinctive flattened bodies and immense size, including the common ocean sunfish Mola mola.
-
C.
Marini
Marini is a French-American actor and former model best known for his roles in "Sex and the City: The Movie" and the TV series "Brothers & Sisters."
-
D.
Manta
Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
-
E.
Manta
Manta is a flying roller coaster at SeaWorld Orlando that simulates the graceful, gliding motion of a manta ray through a combination of high-speed thrills and aquatic theming.
- 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_69e11e52c2048190952dc5df209b9bed |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15bdeae9c8190a5b66e540484db37 |
completed | April 29, 2026, 1:16 a.m. |
Created at: April 16, 2026, 8:48 p.m.