Triple

T49053
Position Surface form Disambiguated ID Type / Status
Subject Rebecca Nurse E964 entity
Predicate givenName P17 FINISHED
Object Rebecca
Rebecca is a feminine given name of Hebrew origin meaning “to tie” or “to bind,” widely used in English-speaking countries.
E3910 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: Rebecca | Statement: [Rebecca Nurse, givenName, Rebecca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rebecca
Context triple: [Rebecca Nurse, givenName, Rebecca]
  • A. Shirley
    Shirley is a small town in north-central Massachusetts served by commuter rail on the MBTA Fitchburg Line.
  • B. Atonement
    Atonement is the Christian theological concept describing how Jesus Christ’s life, death, and resurrection reconcile humanity with God and address the problem of sin.
  • C. Waverley
    Waverley is a commuter rail station in Belmont, Massachusetts, served by the MBTA’s Fitchburg Line.
  • D. Mens et Manus
    Mens et Manus is the Latin motto of the Massachusetts Institute of Technology, expressing the union of mind and hand in the pursuit of knowledge and practical application.
  • E. The Quaker
    The Quaker is the traditional, colonial-era–styled mascot representing the University of Pennsylvania and its athletic teams.
  • 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: Rebecca
Triple: [Rebecca Nurse, givenName, Rebecca]
Generated description
Rebecca is a feminine given name of Hebrew origin meaning “to tie” or “to bind,” widely used in English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Rebecca
Target entity description: Rebecca is a feminine given name of Hebrew origin meaning “to tie” or “to bind,” widely used in English-speaking countries.
  • A. Shirley
    Shirley is a small town in north-central Massachusetts served by commuter rail on the MBTA Fitchburg Line.
  • B. Atonement
    Atonement is the Christian theological concept describing how Jesus Christ’s life, death, and resurrection reconcile humanity with God and address the problem of sin.
  • C. Her Majesty
    Her Majesty is the formal royal style used to address or refer to a reigning queen such as Anne, Queen of Great Britain.
  • D. Red Room
    The Red Room is an elegant, historically significant parlor in the White House used for receptions and small gatherings, distinguished by its red décor and antique furnishings.
  • E. Waverley
    Waverley is a commuter rail station in Belmont, Massachusetts, served by the MBTA’s Fitchburg Line.
  • 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_69a2480baefc81909951b14058479aa2 completed Feb. 28, 2026, 1:42 a.m.
NER Named-entity recognition batch_69a24af3e4a88190abe2f0c5a0e83ff3 completed Feb. 28, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69a24e659ac48190a11b70a85867d784 completed Feb. 28, 2026, 2:09 a.m.
NEDg Description generation batch_69a24eff3f0881909b46502175682d99 completed Feb. 28, 2026, 2:12 a.m.
NED2 Entity disambiguation (via description) batch_69a2542d9b388190bcc4581c3b79aa51 completed Feb. 28, 2026, 2:34 a.m.
Created at: Feb. 28, 2026, 1:47 a.m.