Triple

T16742668
Position Surface form Disambiguated ID Type / Status
Subject Kentucky Legislative Research Commission E406870 entity
Predicate alsoKnownAs P39 FINISHED
Object LRC
LRC is the commonly used abbreviation for the Kentucky Legislative Research Commission, the nonpartisan support agency for the Kentucky General Assembly.
 E1230295 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: LRC | Statement: [Kentucky Legislative Research Commission, alsoKnownAs, LRC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LRC
Context triple: [Kentucky Legislative Research Commission, alsoKnownAs, LRC]
  • A. LRCX
    LRCX is the stock ticker symbol for Lam Research Corporation, a leading U.S.-based supplier of semiconductor manufacturing equipment.
  • B. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • C. LRCN
    LRCN is a deep learning architecture that combines convolutional neural networks with recurrent neural networks to model and interpret visual sequences such as video and image descriptions.
  • D. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • E. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • 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: LRC
Triple: [Kentucky Legislative Research Commission, alsoKnownAs, LRC]
Generated description
LRC is the commonly used abbreviation for the Kentucky Legislative Research Commission, the nonpartisan support agency for the Kentucky General Assembly.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LRC
Target entity description: LRC is the commonly used abbreviation for the Kentucky Legislative Research Commission, the nonpartisan support agency for the Kentucky General Assembly.
  • A. LRCX
    LRCX is the stock ticker symbol for Lam Research Corporation, a leading U.S.-based supplier of semiconductor manufacturing equipment.
  • B. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • C. LRCN
    LRCN is a deep learning architecture that combines convolutional neural networks with recurrent neural networks to model and interpret visual sequences such as video and image descriptions.
  • D. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • E. LR
    LR is the ISO 3166-1 alpha-2 country code for Liberia, a West African nation on the Atlantic coast.
  • 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_69d8838ffb088190a0b11149929006bf completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e39c3f49808190b543d8da34031f3d completed April 18, 2026, 2:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009d52d88081909695a08d00bd2257 completed May 10, 2026, 2:59 p.m.
NEDg Description generation batch_6a009dd925308190a82c6ef014b37333 completed May 10, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_6a009e9b9874819084060408cdc44d0b completed May 10, 2026, 3:04 p.m.
Created at: April 10, 2026, 5:21 a.m.