Triple

T9749274
Position Surface form Disambiguated ID Type / Status
Subject Les Républicains E236397 entity
Predicate shortName P43 FINISHED
Object LR
LR is the commonly used abbreviation for Les Républicains, a major center-right political party in France.
 E819030 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: LR | Statement: [Les Républicains, shortName, LR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LR
Context triple: [Les Républicains, shortName, LR]
  • A. LR
    LR is the ISO 3166-1 alpha-2 country code for Liberia, a West African nation on the Atlantic coast.
  • B. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • C. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • D. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • E. RL
    RL is the commonly used acronym for the U.S. Department of Energy’s Richland Operations Office, which oversees environmental cleanup and related activities at the Hanford Site in Washington State.
  • 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: LR
Triple: [Les Républicains, shortName, LR]
Generated description
LR is the commonly used abbreviation for Les Républicains, a major center-right political party in France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LR
Target entity description: LR is the commonly used abbreviation for Les Républicains, a major center-right political party in France.
  • A. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • B. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • C. LR
    LR is the ISO 3166-1 alpha-2 country code for Liberia, a West African nation on the Atlantic coast.
  • D. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • E. RL
    RL is the commonly used acronym for the U.S. Department of Energy’s Richland Operations Office, which oversees environmental cleanup and related activities at the Hanford Site in Washington State.
  • 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_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f6a2f8c8190a6f6af6587ee90b8 completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1b01678f88190900a941b9d111c58 completed April 5, 2026, 12:43 a.m.
NEDg Description generation batch_69d1b13a7b0c8190a526bffdc4caf73d completed April 5, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_69d1b1cdc91481908e98c4d5cdec785b completed April 5, 2026, 12:50 a.m.
Created at: March 30, 2026, 8:23 p.m.