Triple

T18766738
Position Surface form Disambiguated ID Type / Status
Subject University of the Philippines Baguio E458911 entity
Predicate hasNickname P39 FINISHED
Object UPB
UPB is the Baguio-based constituent campus of the University of the Philippines system, known for its strong programs in the arts, sciences, and Cordillera studies.
 E1341862 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: UPB | Statement: [University of the Philippines Baguio, hasNickname, UPB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UPB
Context triple: [University of the Philippines Baguio, hasNickname, UPB]
  • A. UPB
    UPB is the commonly used abbreviation for the Politehnica University of Bucharest, a major technical university in Romania.
  • B. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • C. UPG
    UPG is the IATA airport code for Sultan Hasanuddin International Airport serving Makassar in South Sulawesi, Indonesia.
  • D. UPE
    UPE is the abbreviation for Université Paris-Est, a French higher education and research institution located in the eastern part of the Paris metropolitan area.
  • E. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • 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: UPB
Triple: [University of the Philippines Baguio, hasNickname, UPB]
Generated description
UPB is the Baguio-based constituent campus of the University of the Philippines system, known for its strong programs in the arts, sciences, and Cordillera studies.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UPB
Target entity description: UPB is the Baguio-based constituent campus of the University of the Philippines system, known for its strong programs in the arts, sciences, and Cordillera studies.
  • A. UPB
    UPB is the commonly used abbreviation for the Politehnica University of Bucharest, a major technical university in Romania.
  • B. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • C. UPG
    UPG is the IATA airport code for Sultan Hasanuddin International Airport serving Makassar in South Sulawesi, Indonesia.
  • D. UPE
    UPE is the abbreviation for Université Paris-Est, a French higher education and research institution located in the eastern part of the Paris metropolitan area.
  • E. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • 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_69d8d395dba0819087568404508590cb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e58d84cfa881909a1d3f7a5ab82574 completed April 20, 2026, 2:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a053d42a8cc8190b1f738ab62b3977b completed May 14, 2026, 3:10 a.m.
NEDg Description generation batch_6a0540f000788190a1e5c3dc04cdc2b9 completed May 14, 2026, 3:26 a.m.
NED2 Entity disambiguation (via description) batch_6a0541b4d92881908445f032e2196a62 completed May 14, 2026, 3:29 a.m.
Created at: April 10, 2026, 11:52 a.m.