Triple

T9732241
Position Surface form Disambiguated ID Type / Status
Subject Paris Métro Line 3 E235772 entity
Predicate hasStation P35 FINISHED
Object Villiers
Villiers is a Paris Métro station in the 8th and 17th arrondissements, serving as an interchange between lines 2 and 3.
 E816337 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: Villiers | Statement: [Paris Métro Line 3, hasStation, Villiers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Villiers
Context triple: [Paris Métro Line 3, hasStation, Villiers]
  • A. Villiers
    Villiers is a prominent English aristocratic surname historically associated with influential political figures and members of the nobility.
  • B. Talbot
    Talbot is a surname of English and Norman origin, historically associated with several notable families and individuals.
  • C. Heydon
    Heydon is a surname and place name of English origin, associated with various locations and families in the United Kingdom.
  • D. Hailwood
    Hailwood is the surname of legendary British motorcycle racer Mike Hailwood, widely regarded as one of the greatest riders in the history of the sport.
  • E. Surtees
    Surtees is an English surname historically associated with notable figures in British literature, motorsport, and regional history.
  • 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: Villiers
Triple: [Paris Métro Line 3, hasStation, Villiers]
Generated description
Villiers is a Paris Métro station in the 8th and 17th arrondissements, serving as an interchange between lines 2 and 3.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Villiers
Target entity description: Villiers is a Paris Métro station in the 8th and 17th arrondissements, serving as an interchange between lines 2 and 3.
  • A. Villiers
    Villiers is a prominent English aristocratic surname historically associated with influential political figures and members of the nobility.
  • B. Talbot
    Talbot is a surname of English and Norman origin, historically associated with several notable families and individuals.
  • C. Heydon
    Heydon is a surname and place name of English origin, associated with various locations and families in the United Kingdom.
  • D. Hailwood
    Hailwood is the surname of legendary British motorcycle racer Mike Hailwood, widely regarded as one of the greatest riders in the history of the sport.
  • E. Surtees
    Surtees is an English surname historically associated with notable figures in British literature, motorsport, and regional history.
  • 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_69ca84d0fad481909cdd45aa77416c48 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9eb3d6e4819090b3c7fb92550c57 completed April 1, 2026, 10:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19fbbba2081909a15725a68423162 completed April 4, 2026, 11:33 p.m.
NEDg Description generation batch_69d1a065ce008190985b792302daa7cb completed April 4, 2026, 11:36 p.m.
NED2 Entity disambiguation (via description) batch_69d1a0f811fc8190b6a46a0441159089 completed April 4, 2026, 11:38 p.m.
Created at: March 30, 2026, 8:22 p.m.