Triple

T33171781
Position Surface form Disambiguated ID Type / Status
Subject Jane Bryan E849052 entity
Predicate birthName P65 FINISHED
Object Jane O’Brien
Jane O’Brien, better known by her stage name Jane Bryan, was an American film actress active in the late 1930s and early 1940s.
E2049300 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: Jane O’Brien | Statement: [Jane Bryan, birthName, Jane O’Brien]
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: Jane O’Brien
Triple: [Jane Bryan, birthName, Jane O’Brien]
Generated description
Jane O’Brien, better known by her stage name Jane Bryan, was an American film actress active in the late 1930s and early 1940s.

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_69f3495be8808190bbf427733df08aad completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d956408481908e95e80cbd9908d3 completed May 3, 2026, 5:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3576cf90c88190ad6db9233638051a completed June 19, 2026, 5:05 p.m.
NEDg Description generation batch_6a357780ccf88190bf11e9d5de9b3025 completed June 19, 2026, 5:08 p.m.
NED2 Entity disambiguation (via description) batch_6a35788be1508190b1794f59d4c78502 completed June 19, 2026, 5:12 p.m.
Created at: May 1, 2026, 1:29 a.m.