Triple

T33343827
Position Surface form Disambiguated ID Type / Status
Subject Beware of Pity E853742 entity
Predicate protagonist P268 FINISHED
Object Anton Hofmiller
Anton Hofmiller is the guilt-ridden young Austrian officer whose tragic entanglement with a disabled woman drives the psychological drama of Stefan Zweig’s novel "Beware of Pity."
E2297339 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: Anton Hofmiller | Statement: [Beware of Pity, protagonist, Anton Hofmiller]
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: Anton Hofmiller
Triple: [Beware of Pity, protagonist, Anton Hofmiller]
Generated description
Anton Hofmiller is the guilt-ridden young Austrian officer whose tragic entanglement with a disabled woman drives the psychological drama of Stefan Zweig’s novel "Beware of Pity."

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_69f3496a1a588190bad9cbe9221144e0 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6df6e5604819089a417abae1dbf23 completed May 3, 2026, 5:38 a.m.
NED1 Entity disambiguation (via context triple) batch_6a8364d374e48190abab4b706bea5557 completed Aug. 17, 2026, 7:45 p.m.
NEDg Description generation batch_6a83657106888190a6f067464b1b8a17 completed Aug. 17, 2026, 7:48 p.m.
NED2 Entity disambiguation (via description) batch_6a8365c6ce3881908d086611929e121e completed Aug. 17, 2026, 7:49 p.m.
Created at: May 1, 2026, 1:34 a.m.