Triple

T10258153
Position Surface form Disambiguated ID Type / Status
Subject Pierre Boulanger E240526 entity
Predicate hasCastMemberOf P7010 FINISHED
Object Monsieur Ibrahim E29833 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: Monsieur Ibrahim | Statement: [Pierre Boulanger, hasCastMemberOf, Monsieur Ibrahim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monsieur Ibrahim
Context triple: [Pierre Boulanger, hasCastMemberOf, Monsieur Ibrahim]
  • A. Monsieur Ibrahim chosen
    Monsieur Ibrahim is a 2003 French drama film in which Omar Sharif delivers an acclaimed performance as a wise Turkish shopkeeper who befriends a lonely Parisian boy.
  • B. Chérif
    Chérif is a family name of Arabic origin historically associated with notable North African figures such as Ahmed Bey ben Mohamed Chérif.
  • C. Mr. Arabin
    Mr. Arabin is a clergyman and academic who becomes a central romantic interest in Anthony Trollope’s novel "Barchester Towers."
  • D. Mounir
    Mounir is a masculine given name of Arabic origin, commonly used in various Arabic-speaking and Muslim-majority countries.
  • E. Saïd
    Saïd is a masculine given name of Arabic origin, commonly used in various forms across the Middle East and North Africa.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasCastMemberOf
Context triple: [Pierre Boulanger, hasCastMemberOf, Monsieur Ibrahim]
  • A. hasCrewMember
    Indicates that an entity includes or employs another entity as a member of its crew.
  • B. includesCastMembersFrom
    Indicates that one entity’s cast list contains one or more cast members who also appear in the cast list of another entity.
  • C. hasCrewRole
    Indicates that an entity serves in a specific role or position within a crew associated with another entity.
  • D. hasCast
    Indicates that a creative work features a particular group of performers or actors.
  • E. notableCastMember chosen
    Indicates that an entity is a member of the cast of another entity (such as a film, show, or production) and is considered particularly notable or significant in that role.
  • F. None of above.

Provenance (4 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_69d381a7e198819090280d5ab885d59e completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d2b5853081909cd0397e08a0f44d completed April 7, 2026, 9:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69d74ff21edc8190b8b4a6967510a869 completed April 9, 2026, 7:06 a.m.
PD Predicate disambiguation batch_69d4d1edae6881909a65201b8e51ea0a completed April 7, 2026, 9:44 a.m.
Created at: April 6, 2026, 11:31 a.m.