Triple

T27490453
Position Surface form Disambiguated ID Type / Status
Subject Crazy Like a Fox E693868 entity
Predicate starring P1507 FINISHED
Object Robyn Douglass
Robyn Douglass is an American actress best known for her film and television work in the late 1970s and 1980s, including roles in projects like "Breaking Away" and "Galactica 1980."
E1831112 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: Robyn Douglass | Statement: [Crazy Like a Fox, starring, Robyn Douglass]
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: Robyn Douglass
Triple: [Crazy Like a Fox, starring, Robyn Douglass]
Generated description
Robyn Douglass is an American actress best known for her film and television work in the late 1970s and 1980s, including roles in projects like "Breaking Away" and "Galactica 1980."

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_69ef5382b9648190be0b1ef2ad5d043c completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62e882cbc81908caa3e8a5fb5c1f4 completed May 2, 2026, 5:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf0dfdbc8190aecca52356449b9b completed June 1, 2026, 12:15 a.m.
NEDg Description generation batch_6a1ccff86fc88190b1438e77f3a5f101 completed June 1, 2026, 12:19 a.m.
NED2 Entity disambiguation (via description) batch_6a24946ccd908190ae144fbc7010aca9 completed June 6, 2026, 9:43 p.m.
Created at: April 27, 2026, 1:05 p.m.