Triple
T7589257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valenzuela |
E179693
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Manuel Valenzuela
Manuel Valenzuela is a notable individual who carries the Valenzuela surname, recognized for his contributions associated with that family name.
|
E692328
|
NE FINISHED |
How this triple was built (4 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: Manuel Valenzuela | Statement: [Valenzuela, hasNotableBearer, Manuel Valenzuela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manuel Valenzuela Context triple: [Valenzuela, hasNotableBearer, Manuel Valenzuela]
-
A.
Luis Valenzuela
Luis Valenzuela is a notable individual distinguished enough to be recognized as a prominent bearer of the Valenzuela surname.
-
B.
Manuel Vega
Manuel Vega is a designer best known for his work on the Moonman character.
-
C.
Manuel Rojas
Manuel Rojas was a 19th-century Puerto Rican revolutionary best known for leading the Grito de Lares uprising for the island’s independence from Spanish colonial rule.
-
D.
Manuel Rojas
Manuel Rojas was the husband of American film actress and model Martha Vickers.
-
E.
Francisco Bringas
Francisco Bringas is a central bourgeois civil servant character in Benito Pérez Galdós’s realist novel *La de Bringas*, embodying the social and moral tensions of 19th-century Madrid.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Manuel Valenzuela Triple: [Valenzuela, hasNotableBearer, Manuel Valenzuela]
Generated description
Manuel Valenzuela is a notable individual who carries the Valenzuela surname, recognized for his contributions associated with that family name.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Manuel Valenzuela Target entity description: Manuel Valenzuela is a notable individual who carries the Valenzuela surname, recognized for his contributions associated with that family name.
-
A.
Luis Valenzuela
Luis Valenzuela is a notable individual distinguished enough to be recognized as a prominent bearer of the Valenzuela surname.
-
B.
Manuel Vega
Manuel Vega is a designer best known for his work on the Moonman character.
-
C.
Manuel Rojas
Manuel Rojas was a 19th-century Puerto Rican revolutionary best known for leading the Grito de Lares uprising for the island’s independence from Spanish colonial rule.
-
D.
Manuel Rojas
Manuel Rojas was the husband of American film actress and model Martha Vickers.
-
E.
Francisco Bringas
Francisco Bringas is a central bourgeois civil servant character in Benito Pérez Galdós’s realist novel *La de Bringas*, embodying the social and moral tensions of 19th-century Madrid.
- F. None of above. chosen
Provenance (5 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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f99991948190af1fb0635895ad94 |
completed | March 27, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ca0812dcec819080c8386061d913b3 |
completed | March 30, 2026, 5:20 a.m. |
| NEDg | Description generation | batch_69ca095a865481908e0ca0e94c5fef0f |
completed | March 30, 2026, 5:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ca09c9b764819096d01c2658ef65e2 |
completed | March 30, 2026, 5:27 a.m. |
Created at: March 27, 2026, 3:52 p.m.