Triple

T11869644
Position Surface form Disambiguated ID Type / Status
Subject Yvette Mimieux E282371 entity
Predicate portrayedCharacter P1668 FINISHED
Object Weena
Weena is the gentle, childlike Eloi woman from H. G. Wells' science fiction novel "The Time Machine," best known from its film adaptations.
 E951095 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: Weena | Statement: [Yvette Mimieux, portrayedCharacter, Weena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Weena
Context triple: [Yvette Mimieux, portrayedCharacter, Weena]
  • A. Alwina
    Alwina is the naive Tunisian shepherdess who becomes a sophisticated Parisian socialite in the 1935 French film "Princesse Tam-Tam."
  • B. Aravan
    Aravan is a heroic figure from the Indian epic Mahabharata, revered in various regional traditions and often associated with themes of sacrifice and devotion.
  • C. Amba
    Amba is a Hindu goddess, widely revered in western India as a fierce yet protective mother deity often worshipped during Navratri.
  • D. Rawene
    Rawene is a small historic town in New Zealand known for its scenic harbour setting on the Hokianga Harbour in the Northland Region.
  • E. Naamah
    Naamah is a woman mentioned in the Hebrew Bible as the Ammonite mother of King Rehoboam of Judah.
  • 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: Weena
Triple: [Yvette Mimieux, portrayedCharacter, Weena]
Generated description
Weena is the gentle, childlike Eloi woman from H. G. Wells' science fiction novel "The Time Machine," best known from its film adaptations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Weena
Target entity description: Weena is the gentle, childlike Eloi woman from H. G. Wells' science fiction novel "The Time Machine," best known from its film adaptations.
  • A. Alwina
    Alwina is the naive Tunisian shepherdess who becomes a sophisticated Parisian socialite in the 1935 French film "Princesse Tam-Tam."
  • B. Aravan
    Aravan is a heroic figure from the Indian epic Mahabharata, revered in various regional traditions and often associated with themes of sacrifice and devotion.
  • C. Amba
    Amba is a Hindu goddess, widely revered in western India as a fierce yet protective mother deity often worshipped during Navratri.
  • D. Rawene
    Rawene is a small historic town in New Zealand known for its scenic harbour setting on the Hokianga Harbour in the Northland Region.
  • E. Naamah
    Naamah is a woman mentioned in the Hebrew Bible as the Ammonite mother of King Rehoboam of Judah.
  • 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_69d6ab2945d081908a5851c916cbcfb5 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a73c04e4819084c0b2ff8e5d2f04 completed April 10, 2026, 7:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69f281a2abfc8190a4769e637dedaaab completed April 29, 2026, 10:09 p.m.
NEDg Description generation batch_69f28a92ac90819092eef473a49d329e completed April 29, 2026, 10:47 p.m.
NED2 Entity disambiguation (via description) batch_69f28c462d888190a43e042b00921dbe completed April 29, 2026, 10:55 p.m.
Created at: April 8, 2026, 9:43 p.m.