Triple
T12054554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teófilo Cubillas |
E287005
|
entity |
| Predicate | nickName |
P2937
|
FINISHED |
| Object |
El Nene
El Nene is the nickname of Peruvian football legend Teófilo Cubillas, one of the greatest attacking midfielders in South American history.
|
E961243
|
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: El Nene | Statement: [Teófilo Cubillas, nickName, El Nene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: El Nene Context triple: [Teófilo Cubillas, nickName, El Nene]
-
A.
Niña
Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
-
B.
Niño
Niño is a Spanish surname commonly borne by individuals and families in Spanish-speaking countries.
-
C.
Nena
Nena is a German pop singer and actress best known internationally for her 1983 hit song "99 Luftballons."
-
D.
Nenê
Nenê is a Brazilian professional basketball player and longtime NBA center known for his physical interior play and key contributions to both the Denver Nuggets and Washington Wizards.
-
E.
Bebeto
Bebeto is a retired Brazilian footballer and prolific striker best known for his successful international career with Brazil, including winning the 1994 FIFA World Cup.
- 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: El Nene Triple: [Teófilo Cubillas, nickName, El Nene]
Generated description
El Nene is the nickname of Peruvian football legend Teófilo Cubillas, one of the greatest attacking midfielders in South American history.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: El Nene Target entity description: El Nene is the nickname of Peruvian football legend Teófilo Cubillas, one of the greatest attacking midfielders in South American history.
-
A.
Niña
Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
-
B.
Niño
Niño is a Spanish surname commonly borne by individuals and families in Spanish-speaking countries.
-
C.
Nena
Nena is a German pop singer and actress best known internationally for her 1983 hit song "99 Luftballons."
-
D.
Nenê
Nenê is a Brazilian professional basketball player and longtime NBA center known for his physical interior play and key contributions to both the Denver Nuggets and Washington Wizards.
-
E.
Bebeto
Bebeto is a retired Brazilian footballer and prolific striker best known for his successful international career with Brazil, including winning the 1994 FIFA World Cup.
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90425258c8190ba7b3b837c439253 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49ddde6548190adae2a889ec5c72b |
completed | May 1, 2026, 12:34 p.m. |
| NEDg | Description generation | batch_69f53d95d4fc8190b5f4e460646bec2a |
completed | May 1, 2026, 11:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f56495830c8190ad5e1767f251b4c5 |
completed | May 2, 2026, 2:42 a.m. |
Created at: April 8, 2026, 9:47 p.m.