Triple

T26907152
Position Surface form Disambiguated ID Type / Status
Subject Rebecca Towne Nurse E677284 entity
Predicate birthName P65 FINISHED
Object Rebecca Towne
Rebecca Towne was a 17th-century English-born woman who became known as Rebecca Nurse, a respected Puritan figure tragically executed during the Salem witch trials.
 E1766277 NE FINISHED

How this triple was built (2 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 Towne | Statement: [Rebecca Towne Nurse, birthName, Rebecca Towne]
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 Towne
Triple: [Rebecca Towne Nurse, birthName, Rebecca Towne]
Generated description
Rebecca Towne was a 17th-century English-born woman who became known as Rebecca Nurse, a respected Puritan figure tragically executed during the Salem witch trials.

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_69eee9bcef1c8190be88586bb902bb9b completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61fd723e08190b08f9f241d8afa7f completed May 2, 2026, 4:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129c84cb4081909ff7ea3adca6d202 completed May 24, 2026, 6:36 a.m.
NEDg Description generation batch_6a129e256d0c8190874e54b63227ce0a completed May 24, 2026, 6:43 a.m.
NED2 Entity disambiguation (via description) batch_6a129ebbbf288190afbb4f21249a6fcf completed May 24, 2026, 6:46 a.m.
Created at: April 27, 2026, 5:59 a.m.