Triple

T9932711
Position Surface form Disambiguated ID Type / Status
Subject Technical Liaison Group E192683 entity
Predicate abbreviation P43 FINISHED
Object TLG
TLG is the abbreviation for the Technical Liaison Group, a coordinating body that facilitates communication and collaboration among various technical standards organizations.
 E831113 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: TLG | Statement: [Technical Liaison Group, abbreviation, TLG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TLG
Context triple: [Technical Liaison Group, abbreviation, TLG]
  • A. Tglg
    Tglg is the ISO 15924 script code assigned to the precolonial Philippine writing system Baybayin.
  • B. TLK
    TLK is a category of long-distance passenger trains in Poland operated by PKP Intercity, typically offering budget-friendly intercity connections.
  • C. TLH
    TLH is the IATA airport code for Tallahassee International Airport, the primary commercial airport serving Florida’s state capital.
  • D. TLF
    TLF is the abbreviation commonly used for the Turkish Land Forces, the main ground warfare branch of Turkey’s military.
  • E. TLA
    TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
  • 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: TLG
Triple: [Technical Liaison Group, abbreviation, TLG]
Generated description
TLG is the abbreviation for the Technical Liaison Group, a coordinating body that facilitates communication and collaboration among various technical standards organizations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TLG
Target entity description: TLG is the abbreviation for the Technical Liaison Group, a coordinating body that facilitates communication and collaboration among various technical standards organizations.
  • A. Tglg
    Tglg is the ISO 15924 script code assigned to the precolonial Philippine writing system Baybayin.
  • B. TLK
    TLK is a category of long-distance passenger trains in Poland operated by PKP Intercity, typically offering budget-friendly intercity connections.
  • C. TLH
    TLH is the IATA airport code for Tallahassee International Airport, the primary commercial airport serving Florida’s state capital.
  • D. TLF
    TLF is the abbreviation commonly used for the Turkish Land Forces, the main ground warfare branch of Turkey’s military.
  • E. TLA
    TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
  • 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_69ca82dd978c8190947124ab0d3315ac completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb5b7897081909b28189aa57af250 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69d228d1620c8190ac7125b268dd6832 completed April 5, 2026, 9:18 a.m.
NEDg Description generation batch_69d22c3a6fc0819083a376736325a04e completed April 5, 2026, 9:32 a.m.
NED2 Entity disambiguation (via description) batch_69d22cabf39881908f45667751384df5 completed April 5, 2026, 9:34 a.m.
Created at: March 30, 2026, 8:43 p.m.