Triple

T1467456
Position Surface form Disambiguated ID Type / Status
Subject La République En Marche! E27056 entity
Predicate shortName P43 FINISHED
Object LREM
LREM is a centrist, pro-European political party in France founded by Emmanuel Macron in 2016.
 E166766 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: LREM | Statement: [La République En Marche!, shortName, LREM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LREM
Context triple: [La République En Marche!, shortName, LREM]
  • A. LIM
    LIM is the IATA airport code for Jorge Chávez International Airport, the main international gateway serving Lima, Peru.
  • B. LEMD
    LEMD is the ICAO airport code for Adolfo Suárez Madrid–Barajas Airport, the main international airport serving Madrid, Spain.
  • C. RLC
    RLC is the Royal Logistic Corps, a branch of the British Army responsible for providing logistics support including supply, transport, and distribution.
  • D. LK
    LK is the two-letter ISO 3166-1 alpha-2 country code assigned to Sri Lanka for international standardization and identification purposes.
  • E. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • 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: LREM
Triple: [La République En Marche!, shortName, LREM]
Generated description
LREM is a centrist, pro-European political party in France founded by Emmanuel Macron in 2016.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LREM
Target entity description: LREM is a centrist, pro-European political party in France founded by Emmanuel Macron in 2016.
  • A. LIM
    LIM is the IATA airport code for Jorge Chávez International Airport, the main international gateway serving Lima, Peru.
  • B. LEMD
    LEMD is the ICAO airport code for Adolfo Suárez Madrid–Barajas Airport, the main international airport serving Madrid, Spain.
  • C. RLC
    RLC is the Royal Logistic Corps, a branch of the British Army responsible for providing logistics support including supply, transport, and distribution.
  • D. LK
    LK is the two-letter ISO 3166-1 alpha-2 country code assigned to Sri Lanka for international standardization and identification purposes.
  • E. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • 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_69a496d25d6881909dbd84f86d763992 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c5bcfa0881909d6137c69825bc7a completed March 1, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e7e7f848190a8abcb73e89e8e34 completed March 8, 2026, 5:51 a.m.
NEDg Description generation batch_69ad0f0b145c8190b08af9a6a3325c23 completed March 8, 2026, 5:54 a.m.
NED2 Entity disambiguation (via description) batch_69ad0f9380648190b4db1b9c92f37831 completed March 8, 2026, 5:56 a.m.
Created at: March 1, 2026, 8:01 p.m.