Triple

T15853622
Position Surface form Disambiguated ID Type / Status
Subject Métropole Européenne de Lille E384400 entity
Predicate shortName P43 FINISHED
Object MEL
MEL is the commonly used acronym for the Métropole Européenne de Lille, the intercommunal metropolitan authority centered on the city of Lille in northern France.
 E1179432 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: MEL | Statement: [Métropole Européenne de Lille, shortName, MEL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MEL
Context triple: [Métropole Européenne de Lille, shortName, MEL]
  • A. MEL
    MEL is the three-letter IATA airport code for Melbourne Airport, the primary international gateway serving Melbourne, Australia.
  • B. Mello
    Mello is the official mascot character created for the 2007 ICC Cricket World Cup held in the West Indies.
  • C. MEG
    MEG is the stock ticker symbol of Megaworld Corporation, a major Philippine real estate developer known for its large-scale township projects.
  • D. mello
    mello is a digital-first mortgage and lending platform developed by loanDepot to streamline and modernize the home loan experience.
  • E. Mel
    Mel is a common shortened form of the given name Melissa, often used as a casual or affectionate nickname.
  • 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: MEL
Triple: [Métropole Européenne de Lille, shortName, MEL]
Generated description
MEL is the commonly used acronym for the Métropole Européenne de Lille, the intercommunal metropolitan authority centered on the city of Lille in northern France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MEL
Target entity description: MEL is the commonly used acronym for the Métropole Européenne de Lille, the intercommunal metropolitan authority centered on the city of Lille in northern France.
  • A. MEL
    MEL is the three-letter IATA airport code for Melbourne Airport, the primary international gateway serving Melbourne, Australia.
  • B. Mello
    Mello is the official mascot character created for the 2007 ICC Cricket World Cup held in the West Indies.
  • C. MEG
    MEG is the stock ticker symbol of Megaworld Corporation, a major Philippine real estate developer known for its large-scale township projects.
  • D. mello
    mello is a digital-first mortgage and lending platform developed by loanDepot to streamline and modernize the home loan experience.
  • E. Mel
    Mel is a common shortened form of the given name Melissa, often used as a casual or affectionate nickname.
  • 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_69d86da422088190aac39e32e6c68429 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e14cae96648190884a85f68b6e9fe1 completed April 16, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa14977408190815ef02cc54075cc completed May 9, 2026, 9:04 p.m.
NEDg Description generation batch_69ffa41a86ec8190b46d541965ecf26e completed May 9, 2026, 9:16 p.m.
NED2 Entity disambiguation (via description) batch_69ffa496f3e48190b8dc82bece548aec completed May 9, 2026, 9:18 p.m.
Created at: April 10, 2026, 4:50 a.m.