Triple

T24458923
Position Surface form Disambiguated ID Type / Status
Subject A Spark of Light E616763 entity
Predicate character P662 FINISHED
Object George Goddard
George Goddard is a central figure in Jodi Picoult’s novel *A Spark of Light*, portrayed as a distraught father whose actions at a women’s reproductive health clinic drive much of the story’s tension and moral complexity.
 E1641181 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: George Goddard | Statement: [A Spark of Light, character, George Goddard]
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: George Goddard
Triple: [A Spark of Light, character, George Goddard]
Generated description
George Goddard is a central figure in Jodi Picoult’s novel *A Spark of Light*, portrayed as a distraught father whose actions at a women’s reproductive health clinic drive much of the story’s tension and moral complexity.

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_69e2d7ef9fe08190a0613908758b4e86 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f298c8d854819091f1d92eef02b1b1 completed April 29, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0ff847fa7c8190bf7f122fd375c008 completed May 22, 2026, 6:31 a.m.
NEDg Description generation batch_6a0ff956f6e48190950c5bace85c9669 completed May 22, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff9feda34819084e79982606c3972 completed May 22, 2026, 6:38 a.m.
Created at: April 18, 2026, 2:19 a.m.