Triple

T27227016
Position Surface form Disambiguated ID Type / Status
Subject Ellen MacArthur E682041 entity
Predicate notableWork P4 FINISHED
Object autobiography "Taking on the World"
"Taking on the World" is the autobiography of British yachtswoman Ellen MacArthur, chronicling her rise in solo long-distance sailing and her record-breaking voyages.
E1759862 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: autobiography "Taking on the World" | Statement: [Ellen MacArthur, notableWork, autobiography "Taking on the World"]
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: autobiography "Taking on the World"
Triple: [Ellen MacArthur, notableWork, autobiography "Taking on the World"]
Generated description
"Taking on the World" is the autobiography of British yachtswoman Ellen MacArthur, chronicling her rise in solo long-distance sailing and her record-breaking voyages.

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_69eefacdad7881908b7bca61c90a1a1e completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6264bcdc08190b0860b3015aac6c6 completed May 2, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1253b123508190b26eb6c6a323378f completed May 24, 2026, 1:26 a.m.
NEDg Description generation batch_6a125456c214819095e03a186301cc5b completed May 24, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_6a1254f46d288190aa6f45f8c8e9007d completed May 24, 2026, 1:31 a.m.
Created at: April 27, 2026, 9:44 a.m.