Triple

T26010368
Position Surface form Disambiguated ID Type / Status
Subject Mullaney E646879 entity
Predicate hasNotableBearer P458 FINISHED
Object James Mullaney
James Mullaney is an American author best known for writing action-adventure and crime fiction, including contributions to "The Destroyer" series and his own "Crag Banyon" detective novels.
E1794361 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: James Mullaney | Statement: [Mullaney, hasNotableBearer, James Mullaney]
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: James Mullaney
Triple: [Mullaney, hasNotableBearer, James Mullaney]
Generated description
James Mullaney is an American author best known for writing action-adventure and crime fiction, including contributions to "The Destroyer" series and his own "Crag Banyon" detective novels.

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_69e77e89d5848190b54352cdb74f6029 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f605b4fa288190981533f604eea508 completed May 2, 2026, 2:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a13031d018081909235882ebde7e850 completed May 24, 2026, 1:54 p.m.
NEDg Description generation batch_6a13041668688190ae7b83c139db490d completed May 24, 2026, 1:58 p.m.
NED2 Entity disambiguation (via description) batch_6a130608e7648190b7666813a297e308 completed May 24, 2026, 2:07 p.m.
Created at: April 22, 2026, 9:01 a.m.