Triple

T32605115
Position Surface form Disambiguated ID Type / Status
Subject Schwarzenegger E833484 entity
Predicate hasNotableBearer P458 FINISHED
Object Joseph Baena Schwarzenegger
Joseph Baena Schwarzenegger is an American fitness model, actor, and real estate agent best known as the son of Arnold Schwarzenegger.
E2020592 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: Joseph Baena Schwarzenegger | Statement: [Schwarzenegger, hasNotableBearer, Joseph Baena Schwarzenegger]
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: Joseph Baena Schwarzenegger
Triple: [Schwarzenegger, hasNotableBearer, Joseph Baena Schwarzenegger]
Generated description
Joseph Baena Schwarzenegger is an American fitness model, actor, and real estate agent best known as the son of Arnold Schwarzenegger.

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_69f3492ab63c8190aec24d5003b47c29 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c6c6357c819093691b67103657ad completed May 3, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34a79588b08190ad48f1468c0f9835 completed June 19, 2026, 2:21 a.m.
NEDg Description generation batch_6a34a833f2508190809ee7c42e2da9d3 completed June 19, 2026, 2:23 a.m.
NED2 Entity disambiguation (via description) batch_6a34a8dde9f48190b9912c18f2470edf completed June 19, 2026, 2:26 a.m.
Created at: May 1, 2026, 1:05 a.m.