Triple

T37947205
Position Surface form Disambiguated ID Type / Status
Subject Jonathan Scarfe E946638 entity
Predicate notableWork P4 FINISHED
Object Radio Free Albemuth
Radio Free Albemuth is a science fiction film adaptation of Philip K. Dick’s novel, exploring themes of authoritarianism, paranoia, and mystical visions in an alternate-reality America.
E2249328 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: Radio Free Albemuth | Statement: [Jonathan Scarfe, notableWork, Radio Free Albemuth]
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: Radio Free Albemuth
Triple: [Jonathan Scarfe, notableWork, Radio Free Albemuth]
Generated description
Radio Free Albemuth is a science fiction film adaptation of Philip K. Dick’s novel, exploring themes of authoritarianism, paranoia, and mystical visions in an alternate-reality America.

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_69f76ef64cf08190ad3e1114b62aac67 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbdb77c688190a813d30e92136a3b completed May 6, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4117fc00508190877a03a6bce4a307 completed June 28, 2026, 12:47 p.m.
NEDg Description generation batch_6a4118c579d08190b3820b73a5d6ba1a completed June 28, 2026, 12:51 p.m.
NED2 Entity disambiguation (via description) batch_6a4119ee31c88190b3905912affd2620 completed June 28, 2026, 12:56 p.m.
Created at: May 3, 2026, 4:20 p.m.