Triple

T4765278
Position Surface form Disambiguated ID Type / Status
Subject Temporal Logic of Actions E105794 entity
Predicate abbreviation P43 FINISHED
Object TLA
TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
 E467807 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: TLA | Statement: [Temporal Logic of Actions, abbreviation, TLA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TLA
Context triple: [Temporal Logic of Actions, abbreviation, TLA]
  • A. TLT
    TLT is the time zone abbreviation used for Timor Leste Time, the standard time observed in East Timor.
  • B. TA
    TA is a common abbreviation for the Territorial Army, a volunteer reserve force that supports a country's regular armed forces.
  • C. TA
    TA is the standard abbreviation for *Transforming Anthropology*, a peer-reviewed academic journal focusing on critical and innovative scholarship in anthropology.
  • D. TA
    TA is the IATA airline designator assigned to TACA Airlines, a major Central American carrier that later merged into Avianca.
  • E. TNLA
    TNLA is the commonly used acronym for the Tamil Nadu Legislative Assembly, the unicameral law-making body of the Indian state of Tamil Nadu.
  • 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: TLA
Triple: [Temporal Logic of Actions, abbreviation, TLA]
Generated description
TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TLA
Target entity description: TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
  • A. TLT
    TLT is the time zone abbreviation used for Timor Leste Time, the standard time observed in East Timor.
  • B. TA
    TA is a common abbreviation for the Territorial Army, a volunteer reserve force that supports a country's regular armed forces.
  • C. TA
    TA is the standard abbreviation for *Transforming Anthropology*, a peer-reviewed academic journal focusing on critical and innovative scholarship in anthropology.
  • D. TA
    TA is the IATA airline designator assigned to TACA Airlines, a major Central American carrier that later merged into Avianca.
  • E. TNLA
    TNLA is the commonly used acronym for the Tamil Nadu Legislative Assembly, the unicameral law-making body of the Indian state of Tamil Nadu.
  • 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_69bd43f226fc8190b867cc249c2a9042 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd65327af48190881c25763232c368 completed March 20, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69be3a87741081909380c51ba4efed92 completed March 21, 2026, 6:28 a.m.
NEDg Description generation batch_69be3d444b888190b2df7433502604ff completed March 21, 2026, 6:40 a.m.
NED2 Entity disambiguation (via description) batch_69be3dd31c648190bfdac15fb85cfec9 completed March 21, 2026, 6:42 a.m.
Created at: March 20, 2026, 1:21 p.m.