Triple
T23212041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosario |
E580621
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | del Rosario |
E1182388
|
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: del Rosario | Statement: [Rosario, hasVariant, del Rosario]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: del Rosario Context triple: [Rosario, hasVariant, del Rosario]
-
A.
del Rosario
chosen
del Rosario is a Spanish-origin surname commonly found in the Philippines and other Spanish-influenced regions.
-
B.
Rojas
Rojas is a Spanish surname historically associated with prominent noble families and political figures in Spain.
-
C.
De Rosario
De Rosario is the surname of Dwayne De Rosario, a prominent Canadian former professional soccer player known for his goal-scoring and playmaking in Major League Soccer.
-
D.
del Pilar
del Pilar is a Filipino surname notably borne by several prominent figures in Philippine history and culture.
-
E.
Dela Cruz
Dela Cruz is a common Spanish-derived surname, especially prevalent in the Philippines and other Spanish-influenced 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_69e2460389408190be74f41d217799a9 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f191620378819096362252c3b819b6 |
completed | April 29, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c30c6a3688190a6569c98399d334f |
completed | May 19, 2026, 9:43 a.m. |
Created at: April 17, 2026, 4:07 p.m.