Triple
T22515941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosario Flores |
E556646
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rosario |
—
|
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: Rosario | Statement: [Rosario Flores, givenName, Rosario]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosario Context triple: [Rosario Flores, givenName, Rosario]
-
A.
Rosario
Rosario is a prestigious private university in Bogotá, Colombia, known for its historic role in the country’s political and academic life.
-
B.
Rosario
Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
-
C.
Rosario
chosen
Rosario is a feminine given name of Spanish and Italian origin, commonly associated with the Roman Catholic devotion to the Rosary.
-
D.
Rosario
Rosario is a coastal municipality in the Mexican state of Sinaloa known for its historic architecture, mining heritage, and proximity to the Pacific Ocean.
-
E.
Rosario
Rosario is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
- 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_69e11e5657e881909f16ca58352c50da |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15e2cfc908190b3489228a1997f45 |
completed | April 29, 2026, 1:26 a.m. |
Created at: April 16, 2026, 8:50 p.m.