Triple

T10474184
Position Surface form Disambiguated ID Type / Status
Subject Royal University of Phnom Penh E247002 entity
Predicate hasAbbreviation P43 FINISHED
Object RUPP
RUPP is Cambodia’s largest and oldest public university, located in Phnom Penh and known for its wide range of undergraduate and graduate programs.
 E864712 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: RUPP | Statement: [Royal University of Phnom Penh, hasAbbreviation, RUPP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RUPP
Context triple: [Royal University of Phnom Penh, hasAbbreviation, RUPP]
  • A. RÜG
    RÜG is the vehicle registration code used for motor vehicles registered in the district of Vorpommern-Rügen in the German state of Mecklenburg-Vorpommern.
  • B. RÜD
    RÜD is the German vehicle registration code for the Rheingau-Taunus-Kreis district in the state of Hesse.
  • C. RAPP
    RAPP is a global marketing and customer experience agency known for its data-driven, personalized communications and direct marketing services.
  • D. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • E. RUT
    RUT is the ticker symbol for the Russell 2000 Index, a major U.S. stock market index tracking the performance of approximately 2,000 small-cap companies.
  • 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: RUPP
Triple: [Royal University of Phnom Penh, hasAbbreviation, RUPP]
Generated description
RUPP is Cambodia’s largest and oldest public university, located in Phnom Penh and known for its wide range of undergraduate and graduate programs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RUPP
Target entity description: RUPP is Cambodia’s largest and oldest public university, located in Phnom Penh and known for its wide range of undergraduate and graduate programs.
  • A. RÜG
    RÜG is the vehicle registration code used for motor vehicles registered in the district of Vorpommern-Rügen in the German state of Mecklenburg-Vorpommern.
  • B. RÜD
    RÜD is the German vehicle registration code for the Rheingau-Taunus-Kreis district in the state of Hesse.
  • C. RAPP
    RAPP is a global marketing and customer experience agency known for its data-driven, personalized communications and direct marketing services.
  • D. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • E. RUT
    RUT is the ticker symbol for the Russell 2000 Index, a major U.S. stock market index tracking the performance of approximately 2,000 small-cap companies.
  • 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_69d381c16c248190a2fe5b471e584e9c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5094e74048190a2c70ef32c50ba71 completed April 7, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8a0140f4c81908ce95b28e09cb04b completed April 10, 2026, 7 a.m.
NEDg Description generation batch_69d8a166404881909c28141fefea2936 completed April 10, 2026, 7:06 a.m.
NED2 Entity disambiguation (via description) batch_69d8a2c550ac81908444c6abfe14698a completed April 10, 2026, 7:12 a.m.
Created at: April 6, 2026, 12:21 p.m.