Triple

T34763750
Position Surface form Disambiguated ID Type / Status
Subject Joel Agee E1002140 entity
Predicate notableWork P4 FINISHED
Object Too Far Afield
Too Far Afield is a novel by German author Günter Grass that explores Germany’s post-reunification era through the lives of aging intelligence agents grappling with historical memory and political change.
 E2111166 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: Too Far Afield | Statement: [Joel Agee, notableWork, Too Far Afield]
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: Too Far Afield
Triple: [Joel Agee, notableWork, Too Far Afield]
Generated description
Too Far Afield is a novel by German author Günter Grass that explores Germany’s post-reunification era through the lives of aging intelligence agents grappling with historical memory and political change.

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_69f76db20dac8190b1e8d0ca4dc1d59f completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f77a1c1cc48190b741eb401c59beca completed May 3, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a376635d4e08190974c34662a4dfe4b completed June 21, 2026, 4:19 a.m.
NEDg Description generation batch_6a3767b4a3f4819092155966b9d94815 completed June 21, 2026, 4:25 a.m.
NED2 Entity disambiguation (via description) batch_6a376835564081909cf34121e4fc8975 completed June 21, 2026, 4:27 a.m.
Created at: May 3, 2026, 3:59 p.m.