Triple

T16018324
Position Surface form Disambiguated ID Type / Status
Subject The Savage Day E388526 entity
Predicate hasCharacter P2308 FINISHED
Object Norah
Norah is a fictional character from Jack Higgins' thriller novel "The Savage Day."
 E1190350 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: Norah | Statement: [The Savage Day, hasCharacter, Norah]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Norah
Context triple: [The Savage Day, hasCharacter, Norah]
  • A. Norah Larkin
    Norah Larkin is the central female protagonist of the 1953 film noir "The Blue Gardenia," whose life unravels after a drunken night leads her to become entangled in a murder investigation.
  • B. Norah Price
    Norah Price is the resilient mechanical engineer and de facto leader portrayed by Kristen Stewart in the 2020 sci‑fi horror film "Underwater," who struggles to survive after a deep-sea drilling station is devastated.
  • C. NORA
    NORA is a U.S. national research framework developed by NIOSH to guide and prioritize occupational safety and health research across key industry sectors.
  • D. Nora
    Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
  • E. Maren
    Maren is a feminine given name of Scandinavian origin, commonly used in Norway and Denmark.
  • 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: Norah
Triple: [The Savage Day, hasCharacter, Norah]
Generated description
Norah is a fictional character from Jack Higgins' thriller novel "The Savage Day."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Norah
Target entity description: Norah is a fictional character from Jack Higgins' thriller novel "The Savage Day."
  • A. Norah Larkin
    Norah Larkin is the central female protagonist of the 1953 film noir "The Blue Gardenia," whose life unravels after a drunken night leads her to become entangled in a murder investigation.
  • B. Norah Price
    Norah Price is the resilient mechanical engineer and de facto leader portrayed by Kristen Stewart in the 2020 sci‑fi horror film "Underwater," who struggles to survive after a deep-sea drilling station is devastated.
  • C. NORA
    NORA is a U.S. national research framework developed by NIOSH to guide and prioritize occupational safety and health research across key industry sectors.
  • D. Nora
    Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
  • E. Maren
    Maren is a feminine given name of Scandinavian origin, commonly used in Norway and Denmark.
  • 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_69d86dabcb7c8190b6a39d6831d2fa1b completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e18296a7008190b72ab2ab02d0fbc9 completed April 17, 2026, 12:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf2a4b0c819094f629c65cf8f880 completed May 10, 2026, 12:19 a.m.
NEDg Description generation batch_69ffcfb02b208190b961b525a29b02a5 completed May 10, 2026, 12:22 a.m.
NED2 Entity disambiguation (via description) batch_69ffd8e7b94c819093bc23288900df33 completed May 10, 2026, 1:01 a.m.
Created at: April 10, 2026, 4:55 a.m.