Triple

T32401852
Position Surface form Disambiguated ID Type / Status
Subject Eddie E827971 entity
Predicate hasOnScreenTrainer P180994 FINISHED
Object Mathilde de Cagny
Mathilde de Cagny is a renowned professional animal trainer best known for working with canine actors in major film and television productions.
 E2014082 NE FINISHED

How this triple was built (3 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: Mathilde de Cagny | Statement: [Eddie, hasOnScreenTrainer, Mathilde de Cagny]
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: Mathilde de Cagny
Triple: [Eddie, hasOnScreenTrainer, Mathilde de Cagny]
Generated description
Mathilde de Cagny is a renowned professional animal trainer best known for working with canine actors in major film and television productions.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasOnScreenTrainer
Context triple: [Eddie, hasOnScreenTrainer, Mathilde de Cagny]
  • A. hasTrainingFor
    Indicates that an entity has received or possesses training that prepares it for performing a specific task, role, or function.
  • B. hasTrainingRole
    Indicates that an entity holds or is assigned a specific role within a training or instructional context.
  • C. hasTrainingTrack
    Indicates that an entity is associated with or assigned to a specific training track or program.
  • D. hasTrainingComplex
    Indicates that an entity possesses or is associated with a dedicated facility or complex used for training activities.
  • E. hasTrainingBaseIn
    Indicates that an entity maintains or operates a training base located in a specified place.
  • F. None of above. chosen

Provenance (7 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_69f34919342c8190a4c3bf35a90d4e58 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f75dc25fa08190b371faf36d9fb72c completed May 3, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3485f392348190b4c1a0505e50083e completed June 18, 2026, 11:57 p.m.
NEDg Description generation batch_6a3486911d8c8190983388d7191b4d77 completed June 19, 2026, midnight
NED2 Entity disambiguation (via description) batch_6a3487efeb248190b0d48dc5266c3927 completed June 19, 2026, 12:06 a.m.
PD Predicate disambiguation batch_69f758586534819083e91172f4bf5098 completed May 3, 2026, 2:14 p.m.
PDg Predicate description generation batch_69f75dc140c4819085063d6c4c36ca61 completed May 3, 2026, 2:37 p.m.
Created at: May 1, 2026, 12:52 a.m.