Triple

T2381505
Position Surface form Disambiguated ID Type / Status
Subject Paris Métro Line 14 E46320 entity
Predicate hasStation P35 FINISHED
Object Madeleine
Madeleine is a Paris Métro station in central Paris that serves as an interchange between several metro lines, including the automated Line 14.
 E271670 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: Madeleine | Statement: [Paris Métro Line 14, hasStation, Madeleine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Madeleine
Context triple: [Paris Métro Line 14, hasStation, Madeleine]
  • A. Madeleine
    Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • B. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • C. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • D. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • E. Laetitia
    Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
  • 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: Madeleine
Triple: [Paris Métro Line 14, hasStation, Madeleine]
Generated description
Madeleine is a Paris Métro station in central Paris that serves as an interchange between several metro lines, including the automated Line 14.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Madeleine
Target entity description: Madeleine is a Paris Métro station in central Paris that serves as an interchange between several metro lines, including the automated Line 14.
  • A. Madeleine
    Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • B. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • C. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • D. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • E. Laetitia
    Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
  • 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_69a88a1554a48190a0180682bcf099be completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc7b98c988190abdb4fe51bf65bde completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f7ac4c8819091132fa9cb265552 completed March 9, 2026, 7:28 p.m.
NEDg Description generation batch_69af200e2db4819085851a45213edc89 completed March 9, 2026, 7:31 p.m.
NED2 Entity disambiguation (via description) batch_69af208dfab081909d706aad8ff5f615 completed March 9, 2026, 7:33 p.m.
Created at: March 4, 2026, 7:57 p.m.