Triple

T26126823
Position Surface form Disambiguated ID Type / Status
Subject De Origine Mali E659126 entity
Predicate associatedPhilosopher P603 FINISHED
Object William King
William King was an Irish philosopher and Anglican Archbishop of Dublin known for his influential work on the problem of evil and free will in early modern philosophy.
E1712356 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: William King | Statement: [De Origine Mali, associatedPhilosopher, William King]
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: William King
Triple: [De Origine Mali, associatedPhilosopher, William King]
Generated description
William King was an Irish philosopher and Anglican Archbishop of Dublin known for his influential work on the problem of evil and free will in early modern philosophy.

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_69ee5bc2b2948190b458ad3f580af779 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f60b8eeeb48190a192fa8ba8cc20bf completed May 2, 2026, 2:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a118f96b900819091ac0262af96ea86 completed May 23, 2026, 11:29 a.m.
NEDg Description generation batch_6a1190e8a4648190b1bf4b5034c42a6c completed May 23, 2026, 11:35 a.m.
NED2 Entity disambiguation (via description) batch_6a1191715cc88190a86e866236503dbc completed May 23, 2026, 11:37 a.m.
Created at: April 26, 2026, 8:12 p.m.