Triple

T11468817
Position Surface form Disambiguated ID Type / Status
Subject Alagnon E271846 entity
Predicate hasTributary P415 FINISHED
Object Violette
Violette is a small river in France that serves as a tributary of the Alagnon.
E928397 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: Violette | Statement: [Alagnon, hasTributary, Violette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Violette
Context triple: [Alagnon, hasTributary, Violette]
  • A. Madame Rouge
    Madame Rouge is a shape-shifting supervillain and sometimes antihero in DC Comics, best known as a key adversary of the Doom Patrol.
  • B. Violeta
    Violeta is a novel by Chilean author Isabel Allende that follows the tumultuous, century-long life of a woman born during the 1918 Spanish flu pandemic.
  • C. Valentinoise
    Valentinoise is the French demonym referring to a female inhabitant of the city of Valence in the Drôme department.
  • D. Fleur
    Fleur is a feminine given name of French origin meaning "flower," often used as a middle name in English-speaking countries.
  • E. Muriel
    Muriel is a feminine given name of French origin that has been borne by various notable figures, including politicians, writers, and artists.
  • 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: Violette
Triple: [Alagnon, hasTributary, Violette]
Generated description
Violette is a small river in France that serves as a tributary of the Alagnon.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Violette
Target entity description: Violette is a small river in France that serves as a tributary of the Alagnon.
  • A. Madame Rouge
    Madame Rouge is a shape-shifting supervillain and sometimes antihero in DC Comics, best known as a key adversary of the Doom Patrol.
  • B. Violeta
    Violeta is a novel by Chilean author Isabel Allende that follows the tumultuous, century-long life of a woman born during the 1918 Spanish flu pandemic.
  • C. Valentinoise
    Valentinoise is the French demonym referring to a female inhabitant of the city of Valence in the Drôme department.
  • D. Fleur
    Fleur is a feminine given name of French origin meaning "flower," often used as a middle name in English-speaking countries.
  • E. Muriel
    Muriel is a feminine given name of French origin that has been borne by various notable figures, including politicians, writers, and artists.
  • 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_69d6aae0c8d881908a5a360c0be3242e completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d822f74144819094479690c8151073 completed April 9, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e60415f6ac8190ad81ed0ef0a30e12 completed April 20, 2026, 10:46 a.m.
NEDg Description generation batch_69e610a07bf881908de79850edb9576f completed April 20, 2026, 11:40 a.m.
NED2 Entity disambiguation (via description) batch_69e617fdaaa88190a1860fb00309596b completed April 20, 2026, 12:11 p.m.
Created at: April 8, 2026, 9:35 p.m.