Triple

T28999626
Position Surface form Disambiguated ID Type / Status
Subject Attack in Black E736264 entity
Predicate hasMember P10 FINISHED
Object Daniel Romano
Daniel Romano is a Canadian singer-songwriter and multi-instrumentalist known for his work in indie rock and alt-country, both as a solo artist and as a member of the band Attack in Black.
E1844382 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: Daniel Romano | Statement: [Attack in Black, hasMember, Daniel Romano]
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: Daniel Romano
Triple: [Attack in Black, hasMember, Daniel Romano]
Generated description
Daniel Romano is a Canadian singer-songwriter and multi-instrumentalist known for his work in indie rock and alt-country, both as a solo artist and as a member of the band Attack in Black.

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_69f077eacd0481908ef0bafd74491cd0 completed April 28, 2026, 9:03 a.m.
NER Named-entity recognition batch_69f65fb930408190a871ef5be8ca99cf completed May 2, 2026, 8:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2505be2f788190bfab59ef13ebb255 completed June 7, 2026, 5:46 a.m.
NEDg Description generation batch_6a2509ba31c8819082af4190ac01be51 completed June 7, 2026, 6:03 a.m.
NED2 Entity disambiguation (via description) batch_6a250e23dd70819082500df27b31e03c completed June 7, 2026, 6:22 a.m.
Created at: April 28, 2026, 9:34 a.m.