Triple

T11293812
Position Surface form Disambiguated ID Type / Status
Subject California Lutheran University E267396 entity
Predicate abbreviation P43 FINISHED
Object CLU
CLU is a private liberal arts university in Thousand Oaks, California, known for its programs in business, education, and the humanities within a Lutheran tradition.
 E917669 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: CLU | Statement: [California Lutheran University, abbreviation, CLU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CLU
Context triple: [California Lutheran University, abbreviation, CLU]
  • A. CLU
    CLU is an early high-level programming language from the 1970s that pioneered data abstraction, iterators, and exception handling, significantly influencing the design of later languages.
  • B. CU
    CU is the common abbreviation for the Christian Union, a Christian student organization found at many universities.
  • C. CU
    CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
  • D. CU
    CU is the commonly used abbreviation for the multi-campus University of Colorado public university system in the United States.
  • E. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • 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: CLU
Triple: [California Lutheran University, abbreviation, CLU]
Generated description
CLU is a private liberal arts university in Thousand Oaks, California, known for its programs in business, education, and the humanities within a Lutheran tradition.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CLU
Target entity description: CLU is a private liberal arts university in Thousand Oaks, California, known for its programs in business, education, and the humanities within a Lutheran tradition.
  • A. CLU
    CLU is an early high-level programming language from the 1970s that pioneered data abstraction, iterators, and exception handling, significantly influencing the design of later languages.
  • B. CU
    CU is the common abbreviation for the Christian Union, a Christian student organization found at many universities.
  • C. CU
    CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
  • D. CU
    CU is the commonly used abbreviation for the multi-campus University of Colorado public university system in the United States.
  • E. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e98b149481909f432a6b9ef8bfbb completed April 9, 2026, 6:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69e50a246a3c81909f4f1d32a1b1efeb completed April 19, 2026, 5 p.m.
NEDg Description generation batch_69e510f7bec08190989118b6e4a7fa49 completed April 19, 2026, 5:29 p.m.
NED2 Entity disambiguation (via description) batch_69e516ac8dec81909c9c1eece372189e completed April 19, 2026, 5:53 p.m.
Created at: April 8, 2026, 9:32 p.m.