Triple

T2449769
Position Surface form Disambiguated ID Type / Status
Subject Chulalongkorn University E53675 entity
Predicate alsoKnownAs P39 FINISHED
Object CU
CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
 E268943 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: CU | Statement: [Chulalongkorn University, alsoKnownAs, CU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CU
Context triple: [Chulalongkorn University, alsoKnownAs, CU]
  • A. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • B. UCE
    UCE is a major public university in Quito, Ecuador, recognized as one of the country’s oldest and most important higher education institutions.
  • C. UC
    UC is a leading Chilean university, widely recognized for its academic excellence and strong influence in education, research, and public policy in Latin America.
  • D. UC
    UC is the final generation of the Holden Torana, a compact Australian car produced in the late 1970s.
  • E. UC
    UC is a public university in Canberra, Australia, known for its career-focused programs and strong industry partnerships.
  • 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: CU
Triple: [Chulalongkorn University, alsoKnownAs, CU]
Generated description
CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CU
Target entity description: CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
  • A. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • B. UCE
    UCE is a major public university in Quito, Ecuador, recognized as one of the country’s oldest and most important higher education institutions.
  • C. UC
    UC is the final generation of the Holden Torana, a compact Australian car produced in the late 1970s.
  • D. UC
    UC is a public university in Canberra, Australia, known for its career-focused programs and strong industry partnerships.
  • E. UC
    UC is a leading Chilean university, widely recognized for its academic excellence and strong influence in education, research, and public policy in Latin America.
  • 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_69ab495d227c8190b26ae6548eeb1019 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd0f2b8488190b1f6a86f0a9f83aa completed March 7, 2026, 7:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0c0069c8190bfb9e71aea4774d3 completed March 9, 2026, 4:09 p.m.
NEDg Description generation batch_69aef53740508190893b14bb1b411a30 completed March 9, 2026, 4:28 p.m.
NED2 Entity disambiguation (via description) batch_69aef9594024819088e7afc0e64429ff completed March 9, 2026, 4:46 p.m.
Created at: March 6, 2026, 9:43 p.m.