Triple
T7038549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | José Ferrer |
E163447
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Ferrer
Ferrer is a Spanish-origin surname borne by numerous notable figures in the arts, sports, and public life.
|
E636597
|
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: Ferrer | Statement: [José Ferrer, familyName, Ferrer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ferrer Context triple: [José Ferrer, familyName, Ferrer]
-
A.
Rafel Nadal i Farreras
Rafel Nadal i Farreras is a Spanish journalist and writer from Mallorca, known for his work in newspapers and his award-winning novels and memoirs.
-
B.
Fernando Lopez
Fernando Lopez was a Filipino politician and businessman who served multiple terms as Vice President of the Philippines in the mid-20th century.
-
C.
Feliciano
Feliciano is a given name of Latin origin, commonly used in Romance-language countries and related to the name Felix.
-
D.
Fernando Cano
Fernando Cano is a member of the historically notable Cano family, a lineage recognized for its enduring social and cultural influence.
-
E.
Jaime de Hoyos
Jaime de Hoyos is an actor known for his role in Robert Rodriguez’s low-budget action film "El Mariachi."
- 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: Ferrer Triple: [José Ferrer, familyName, Ferrer]
Generated description
Ferrer is a Spanish-origin surname borne by numerous notable figures in the arts, sports, and public life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ferrer Target entity description: Ferrer is a Spanish-origin surname borne by numerous notable figures in the arts, sports, and public life.
-
A.
Rafel Nadal i Farreras
Rafel Nadal i Farreras is a Spanish journalist and writer from Mallorca, known for his work in newspapers and his award-winning novels and memoirs.
-
B.
Fernando Lopez
Fernando Lopez was a Filipino politician and businessman who served multiple terms as Vice President of the Philippines in the mid-20th century.
-
C.
Feliciano
Feliciano is a given name of Latin origin, commonly used in Romance-language countries and related to the name Felix.
-
D.
Fernando Cano
Fernando Cano is a member of the historically notable Cano family, a lineage recognized for its enduring social and cultural influence.
-
E.
Jaime de Hoyos
Jaime de Hoyos is an actor known for his role in Robert Rodriguez’s low-budget action film "El Mariachi."
- 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_69c6885e7c1c8190be32a8f79ab4e0cf |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e223077c819097992089fa83c563 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c775ac21888190a1adcf86b49b345f |
completed | March 28, 2026, 6:31 a.m. |
| NEDg | Description generation | batch_69c777f9b41881909566511e97725fde |
completed | March 28, 2026, 6:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c778717e888190a0b2a5914d13cb71 |
completed | March 28, 2026, 6:42 a.m. |
Created at: March 27, 2026, 2:36 p.m.