Triple

T36968914
Position Surface form Disambiguated ID Type / Status
Subject Geoffrey O'Brien E914504 entity
Predicate notableWork P4 FINISHED
Object Red Sky Café
Red Sky Café is a work by writer and critic Geoffrey O'Brien, likely reflecting his characteristic blend of literary insight and atmospheric, genre-bending storytelling.
 E2206916 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: Red Sky Café | Statement: [Geoffrey O'Brien, notableWork, Red Sky Café]
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: Red Sky Café
Triple: [Geoffrey O'Brien, notableWork, Red Sky Café]
Generated description
Red Sky Café is a work by writer and critic Geoffrey O'Brien, likely reflecting his characteristic blend of literary insight and atmospheric, genre-bending storytelling.

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_69f76e8c498c8190b2842db80aea8b3b completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9ff4087408190b780e0bc88754329 completed May 5, 2026, 2:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e2c43e86c8190aaa8369455a02a52 completed June 26, 2026, 7:37 a.m.
NEDg Description generation batch_6a3e2ce08a40819081db007321d0b279 completed June 26, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_6a3e458016e881909cef925bc1bad341 completed June 26, 2026, 9:25 a.m.
Created at: May 3, 2026, 4:14 p.m.