Triple

T19423646
Position Surface form Disambiguated ID Type / Status
Subject Dawn Moore E485923 entity
Predicate givenName P17 FINISHED
Object Dawn
Dawn is a feminine given name commonly associated with the early morning time when light first appears in the sky.
E250040 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: Dawn | Statement: [Dawn Moore, givenName, Dawn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dawn
Context triple: [Dawn Moore, givenName, Dawn]
  • A. Dawn
    Dawn is a novel by Elie Wiesel that explores the moral and psychological struggles of a young Holocaust survivor involved in a Jewish underground movement in British-controlled Palestine.
  • B. Dawn
    Dawn was a NASA space probe that studied the protoplanet Vesta and the dwarf planet Ceres in the asteroid belt using ion propulsion.
  • C. Dawn
    "Dawn" is a lesser-known novel by American author Eleanor H. Porter, best known for writing "Pollyanna."
  • D. Dawn
    Dawn is a leading American dishwashing liquid brand known for its strong grease-cutting power and use in wildlife rescue efforts.
  • E. Dawn
    Dawn is a central character in the crime thriller film "Catch .44," around whom much of the movie’s tense, intersecting plot revolves.
  • 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: Dawn
Triple: [Dawn Moore, givenName, Dawn]
Generated description
Dawn is a feminine given name commonly associated with the early morning time when light first appears in the sky.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Dawn
Target entity description: Dawn is a feminine given name commonly associated with the early morning time when light first appears in the sky.
  • A. Dawn chosen
    Dawn is a feminine given name commonly associated with the early morning time when light first appears in the sky.
  • B. Dawn
    Dawn is a leading American dishwashing liquid brand known for its strong grease-cutting power and use in wildlife rescue efforts.
  • C. Dawn
    Dawn is an American singer, songwriter, and producer best known as a former member of Danity Kane and Diddy – Dirty Money who later established a critically acclaimed solo career in experimental R&B and electronic music.
  • D. Dawn
    Dawn is a science fiction novel by Octavia E. Butler that opens her Xenogenesis (Lilith’s Brood) trilogy, exploring themes of alien contact, genetic manipulation, and the future of humanity.
  • E. Dawn
    Dawn is a central character in the crime thriller film "Catch .44," around whom much of the movie’s tense, intersecting plot revolves.
  • F. None of above.

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_69d8e8d688f881909c85104a62e09d8a completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e632169d9c81909a88704c6beb8fe0 completed April 20, 2026, 2:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0733d40a648190b934f5dfc7b5d44f completed May 15, 2026, 2:55 p.m.
NEDg Description generation batch_6a07352d07a48190b29bfd46c7c93f30 completed May 15, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_6a0735b445388190a588f6eeaf49e1b7 completed May 15, 2026, 3:03 p.m.
Created at: April 10, 2026, 1:37 p.m.