Triple

T8483064
Position Surface form Disambiguated ID Type / Status
Subject Nal Kalchbrenner E200564 entity
Predicate hasPublishedIn P309 FINISHED
Object TACL
TACL (Transactions of the Association for Computational Linguistics) is a leading peer-reviewed journal publishing high-quality research in natural language processing and computational linguistics.
E736214 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: TACL | Statement: [Nal Kalchbrenner, hasPublishedIn, TACL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TACL
Context triple: [Nal Kalchbrenner, hasPublishedIn, TACL]
  • A. TLA
    TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
  • B. TAC
    TAC is the commonly used abbreviation for The Athletic Congress, the former governing body for track and field in the United States.
  • C. TACC
    TACC is an advanced driver-assistance feature that automatically adjusts a vehicle’s speed to maintain a safe following distance from traffic ahead.
  • D. LTAC
    LTAC is the ICAO airport code for Esenboğa International Airport, the main airport serving Ankara, Turkey.
  • E. TALE Conference
    TALE Conference is an annual IEEE international conference focused on advancing engineering and technology education across the Asia-Pacific region.
  • 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: TACL
Triple: [Nal Kalchbrenner, hasPublishedIn, TACL]
Generated description
TACL (Transactions of the Association for Computational Linguistics) is a leading peer-reviewed journal publishing high-quality research in natural language processing and computational linguistics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: TACL
Target entity description: TACL (Transactions of the Association for Computational Linguistics) is a leading peer-reviewed journal publishing high-quality research in natural language processing and computational linguistics.
  • A. TLA
    TLA is a formal specification language developed by Leslie Lamport for describing and reasoning about concurrent and distributed systems using temporal logic.
  • B. TAC
    TAC is the commonly used abbreviation for The Athletic Congress, the former governing body for track and field in the United States.
  • C. TACC
    TACC is an advanced driver-assistance feature that automatically adjusts a vehicle’s speed to maintain a safe following distance from traffic ahead.
  • D. LTAC
    LTAC is the ICAO airport code for Esenboğa International Airport, the main airport serving Ankara, Turkey.
  • E. TALE Conference
    TALE Conference is an annual IEEE international conference focused on advancing engineering and technology education across the Asia-Pacific region.
  • 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_69ca831b17988190a1f3f3413d57b820 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe53845e881909eeb32863c7aa942 completed March 31, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce3a348a8481908a72c7ac15605022 completed April 2, 2026, 9:43 a.m.
NEDg Description generation batch_69ce3b56c3d881909468c3304e84cdb8 completed April 2, 2026, 9:48 a.m.
NED2 Entity disambiguation (via description) batch_69ce3c2579a08190af15d40d4bf7bb9f completed April 2, 2026, 9:51 a.m.
Created at: March 30, 2026, 6:12 p.m.