Triple

T380477
Position Surface form Disambiguated ID Type / Status
Subject SGML E8666 entity
Predicate influenced P9 FINISHED
Object TEI
TEI (Text Encoding Initiative) is a widely used standard for representing and encoding texts in digital form, particularly in the humanities, using structured markup.
 E8666 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: TEI | Statement: [SGML, influenced, TEI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TEI
Context triple: [SGML, influenced, TEI]
  • A. METS
    METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
  • B. TXL
    TXL was the IATA airport code for Berlin Tegel Airport, the former main international airport of Berlin, Germany.
  • C. MARC
    MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
  • D. SGML
    SGML (Standard Generalized Markup Language) is a standardized metalanguage for defining markup languages used to structure and describe the content of electronic documents.
  • E. NMTI
    NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
  • 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: TEI
Triple: [SGML, influenced, TEI]
Generated description
TEI (Text Encoding Initiative) is a widely used standard for representing and encoding texts in digital form, particularly in the humanities, using structured markup.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TEI
Target entity description: TEI (Text Encoding Initiative) is a widely used standard for representing and encoding texts in digital form, particularly in the humanities, using structured markup.
  • A. METS
    METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
  • B. TXL
    TXL was the IATA airport code for Berlin Tegel Airport, the former main international airport of Berlin, Germany.
  • C. MARC
    MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
  • D. SGML chosen
    SGML (Standard Generalized Markup Language) is a standardized metalanguage for defining markup languages used to structure and describe the content of electronic documents.
  • E. NMTI
    NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
  • F. None of above.

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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec2c95088190a603bb1ee076ebd6 completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3faffa5848190b77503516f3d0ba6 completed March 1, 2026, 8:38 a.m.
NEDg Description generation batch_69a3fb912d9881909d8eb02cdae70163 completed March 1, 2026, 8:40 a.m.
NED2 Entity disambiguation (via description) batch_69a3fd99e73c8190b5a92856ca0a4b82 completed March 1, 2026, 8:49 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.