Triple

T13809097
Position Surface form Disambiguated ID Type / Status
Subject Montagne Noire E331835 entity
Predicate nearCity P350 FINISHED
Object Carcassonne E90283 NE FINISHED

How this triple was built (2 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: Carcassonne | Statement: [Montagne Noire, nearCity, Carcassonne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carcassonne
Context triple: [Montagne Noire, nearCity, Carcassonne]
  • A. Carcassonne chosen
    Carcassonne is a historic fortified city in southern France renowned for its medieval citadel with double walls and numerous watchtowers.
  • B. Cité
    Cité is a Paris Métro station located on the Île de la Cité in the historic center of Paris.
  • C. Crevel
    Crevel is a vain, wealthy former perfumer and libertine in Honoré de Balzac’s novel "La Cousine Bette," emblematic of the corrupt bourgeois society he satirizes.
  • D. Casteau
    Casteau is a village in Belgium best known as the site of NATO’s Supreme Headquarters Allied Powers Europe (SHAPE).
  • E. Cour Carrée
    Cour Carrée is the large, historic square courtyard at the eastern end of the Louvre in Paris, surrounded by classical palace façades that reflect the museum’s origins as a royal residence.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d81c59f8808190a851bc56afdc55e9 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de026eae8481908b8880635e6a9152 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b8dc1ec0819098c4f32eb3991613 completed May 3, 2026, 9:06 p.m.
Created at: April 9, 2026, 10:12 p.m.