Triple

T815602
Position Surface form Disambiguated ID Type / Status
Subject Algol 68 E17646 entity
Predicate influenced P9 FINISHED
Object 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.
 E96199 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: [Algol 68, influenced, CLU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CLU
Context triple: [Algol 68, influenced, CLU]
  • A. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • B. UCLS
    UCLS is a renowned private day school in Chicago affiliated with the University of Chicago, known for its progressive education and strong academic programs from nursery through high school.
  • C. UCH
    UCH is a leading public research university in Santiago, Chile, renowned for its academic excellence and significant influence on the country’s intellectual and cultural life.
  • D. CLA
    The Mercedes-Benz CLA is a compact luxury four-door coupé known for its sleek styling, advanced technology, and entry-level positioning within the brand’s lineup.
  • E. CUB
    CUB is the three-letter ISO 3166-1 alpha-3 country code assigned to Cuba for international standardization and identification purposes.
  • 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: [Algol 68, influenced, CLU]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CLU
Target entity description: 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.
  • A. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • B. UCLS
    UCLS is a renowned private day school in Chicago affiliated with the University of Chicago, known for its progressive education and strong academic programs from nursery through high school.
  • C. UCH
    UCH is a leading public research university in Santiago, Chile, renowned for its academic excellence and significant influence on the country’s intellectual and cultural life.
  • D. CLA
    The Mercedes-Benz CLA is a compact luxury four-door coupé known for its sleek styling, advanced technology, and entry-level positioning within the brand’s lineup.
  • E. CUB
    CUB is the three-letter ISO 3166-1 alpha-3 country code assigned to Cuba for international standardization and identification purposes.
  • 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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab5157b08190b6c8f2fd455f261e completed March 1, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d8b0b0c8190a6226d6b8daade25 completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a782eda49c8190bdaf4fb8db685071 completed March 4, 2026, 12:55 a.m.
NED2 Entity disambiguation (via description) batch_69a784f6eee48190a348008b931d545b completed March 4, 2026, 1:03 a.m.
Created at: March 1, 2026, 7:38 p.m.