Triple
T7802940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ochoa |
E180474
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Manuel Ochoa
Manuel Ochoa is a personal name shared by multiple individuals, including figures in fields such as sports, arts, and public life.
|
E711979
|
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 Ochoa | Statement: [Ochoa, hasNotableBearer, Manuel Ochoa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manuel Ochoa Context triple: [Ochoa, hasNotableBearer, Manuel Ochoa]
-
A.
Manuel Vega
Manuel Vega is a designer best known for his work on the Moonman character.
-
B.
Manuel Medina
Manuel Medina is a Mexican former professional boxer and multiple-time featherweight world champion known for his technical skill and durability in the ring.
-
C.
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.
-
D.
Manuel Becerra
Manuel Becerra is a Madrid Metro station serving as an interchange hub on Line 2 and other lines in the eastern part of the city.
-
E.
Manuel Machado
Manuel Machado is a Portuguese football manager known for coaching numerous Primeira Liga clubs over several decades.
- 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 Ochoa Triple: [Ochoa, hasNotableBearer, Manuel Ochoa]
Generated description
Manuel Ochoa is a personal name shared by multiple individuals, including figures in fields such as sports, arts, and public life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Manuel Ochoa Target entity description: Manuel Ochoa is a personal name shared by multiple individuals, including figures in fields such as sports, arts, and public life.
-
A.
Manuel Vega
Manuel Vega is a designer best known for his work on the Moonman character.
-
B.
Manuel Medina
Manuel Medina is a Mexican former professional boxer and multiple-time featherweight world champion known for his technical skill and durability in the ring.
-
C.
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.
-
D.
Manuel Becerra
Manuel Becerra is a Madrid Metro station serving as an interchange hub on Line 2 and other lines in the eastern part of the city.
-
E.
Manuel Machado
Manuel Machado is a Portuguese football manager known for coaching numerous Primeira Liga clubs over several decades.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf635a4648190af907a686d87f073 |
completed | March 30, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc9336e14c8190ad925da158d98596 |
completed | April 1, 2026, 3:38 a.m. |
| NEDg | Description generation | batch_69cc955542fc8190a84be60f4efea915 |
completed | April 1, 2026, 3:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc964c6b308190ae121072b1180268 |
completed | April 1, 2026, 3:51 a.m. |
Created at: March 30, 2026, 4:34 p.m.