Triple

T1250810
Position Surface form Disambiguated ID Type / Status
Subject Unicode Technical Report #29 E26869 entity
Predicate hasAbbreviation P43 FINISHED
Object UTR #29
UTR #29 is a Unicode Technical Report that defines the standard rules and algorithms for text segmentation, such as determining grapheme clusters, words, and sentences in Unicode text.
 E143995 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: UTR #29 | Statement: [Unicode Technical Report #29, hasAbbreviation, UTR #29]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UTR #29
Context triple: [Unicode Technical Report #29, hasAbbreviation, UTR #29]
  • A. The U's
    The U's is the commonly used nickname for Cambridge United Football Club, an English professional football team based in Cambridge.
  • B. UGT
    UGT is a major Spanish trade union confederation representing workers across multiple sectors and advocating for labor rights and social justice.
  • C. Release 99
    Release 99 is the first 3GPP standardization release that defined the initial UMTS (3G) system architecture and capabilities.
  • D. Utraque Unum
    Utraque Unum is the Latin motto of Georgetown University, meaning “Both into One,” expressing the union of different traditions or elements into a harmonious whole.
  • E. U3
    U3 is one of the main lines of the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • 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: UTR #29
Triple: [Unicode Technical Report #29, hasAbbreviation, UTR #29]
Generated description
UTR #29 is a Unicode Technical Report that defines the standard rules and algorithms for text segmentation, such as determining grapheme clusters, words, and sentences in Unicode text.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UTR #29
Target entity description: UTR #29 is a Unicode Technical Report that defines the standard rules and algorithms for text segmentation, such as determining grapheme clusters, words, and sentences in Unicode text.
  • A. The U's
    The U's is the commonly used nickname for Cambridge United Football Club, an English professional football team based in Cambridge.
  • B. UGT
    UGT is a major Spanish trade union confederation representing workers across multiple sectors and advocating for labor rights and social justice.
  • C. Release 99
    Release 99 is the first 3GPP standardization release that defined the initial UMTS (3G) system architecture and capabilities.
  • D. Utraque Unum
    Utraque Unum is the Latin motto of Georgetown University, meaning “Both into One,” expressing the union of different traditions or elements into a harmonious whole.
  • E. U3
    U3 is one of the main lines of the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • 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_69a49487a9c48190ba9b05348fd1b53f completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4bf84c73c8190bbb14265cd7ab6ae completed March 1, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac93c66bf881908f4b63548341178e completed March 7, 2026, 9:08 p.m.
NEDg Description generation batch_69ac943de3f0819085dff5ef12f01766 completed March 7, 2026, 9:10 p.m.
NED2 Entity disambiguation (via description) batch_69ac95c05ab081909db602d7bea73bf4 completed March 7, 2026, 9:16 p.m.
Created at: March 1, 2026, 7:47 p.m.