Triple
T18802660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Baguio |
E459794
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object |
Fernando Bautista
Fernando Bautista is a Filipino educator and entrepreneur best known for establishing the University of Baguio, a major private university in Baguio City, Philippines.
|
E1342862
|
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: Fernando Bautista | Statement: [University of Baguio, founder, Fernando Bautista]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fernando Bautista Context triple: [University of Baguio, founder, Fernando Bautista]
-
A.
Francisco Cano
Francisco Cano is a personal name shared by several individuals, most commonly found in Spanish-speaking countries.
-
B.
Nicolás Almagro
Nicolás Almagro is a retired Spanish professional tennis player best known for his powerful one-handed backhand and success on clay courts, including multiple ATP titles and top-10 world rankings.
-
C.
Santiago Cabrera
Santiago Cabrera is a Chilean actor best known for his roles in television series such as "Heroes," "Merlin," and "Star Trek: Picard."
-
D.
Pablo González
Pablo González is an editor known for his work on the film "Tideland."
-
E.
Roberto Etcheverry
Roberto Etcheverry is a Chilean actor known for his work in television, film, and theater.
- 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: Fernando Bautista Triple: [University of Baguio, founder, Fernando Bautista]
Generated description
Fernando Bautista is a Filipino educator and entrepreneur best known for establishing the University of Baguio, a major private university in Baguio City, Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fernando Bautista Target entity description: Fernando Bautista is a Filipino educator and entrepreneur best known for establishing the University of Baguio, a major private university in Baguio City, Philippines.
-
A.
Francisco Cano
Francisco Cano is a personal name shared by several individuals, most commonly found in Spanish-speaking countries.
-
B.
Nicolás Almagro
Nicolás Almagro is a retired Spanish professional tennis player best known for his powerful one-handed backhand and success on clay courts, including multiple ATP titles and top-10 world rankings.
-
C.
Santiago Cabrera
Santiago Cabrera is a Chilean actor best known for his roles in television series such as "Heroes," "Merlin," and "Star Trek: Picard."
-
D.
Pablo González
Pablo González is an editor known for his work on the film "Tideland."
-
E.
Roberto Etcheverry
Roberto Etcheverry is a Chilean actor known for his work in television, film, and theater.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a0253f748190998995e3b1524357 |
completed | April 20, 2026, 3:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a054725edbc819099ad8200394653ec |
completed | May 14, 2026, 3:53 a.m. |
| NEDg | Description generation | batch_6a054828f894819093b66caebaf0399d |
completed | May 14, 2026, 3:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0548f1ed74819090775763f4976a1a |
completed | May 14, 2026, 4 a.m. |
Created at: April 10, 2026, 11:53 a.m.