Triple

T16013583
Position Surface form Disambiguated ID Type / Status
Subject Lori Martin E388402 entity
Predicate hasGivenName P17 FINISHED
Object Dawn
Dawn is a feminine given name commonly associated with the first appearance of light in the morning and often chosen for its connotations of new beginnings and hope.
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: [Lori Martin, hasGivenName, Dawn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dawn
Context triple: [Lori Martin, hasGivenName, 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: [Lori Martin, hasGivenName, Dawn]
Generated description
Dawn is a feminine given name commonly associated with the first appearance of light in the morning and often chosen for its connotations of new beginnings and hope.
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 first appearance of light in the morning and often chosen for its connotations of new beginnings and hope.
  • 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 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.
  • D. Dawn
    Dawn is a central character in the crime thriller film "Catch .44," around whom much of the movie’s tense, intersecting plot revolves.
  • E. 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.
  • 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_69d86dabcb7c8190b6a39d6831d2fa1b completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e18292b79881908efac869603c4029 completed April 17, 2026, 12:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf267a9c81908f1fa1ad117c5e2c completed May 10, 2026, 12:19 a.m.
NEDg Description generation batch_69ffd0bfc05881908d7223c52050ea14 completed May 10, 2026, 12:26 a.m.
NED2 Entity disambiguation (via description) batch_69ffd159dbcc81908ac586a6b8de57cf completed May 10, 2026, 12:29 a.m.
Created at: April 10, 2026, 4:55 a.m.