Triple

T34610108
Position Surface form Disambiguated ID Type / Status
Subject Owen Brewster E888702 entity
Predicate positionHeld P8 FINISHED
Object Member of the Maine House of Representatives
A Member of the Maine House of Representatives is an elected state legislator who serves in the lower chamber of Maine’s bicameral legislature, helping to create and pass state laws.
E2104239 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: Member of the Maine House of Representatives | Statement: [Owen Brewster, positionHeld, Member of the Maine House of Representatives]
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: Member of the Maine House of Representatives
Triple: [Owen Brewster, positionHeld, Member of the Maine House of Representatives]
Generated description
A Member of the Maine House of Representatives is an elected state legislator who serves in the lower chamber of Maine’s bicameral legislature, helping to create and pass state laws.

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_69f349d489d48190ba30e7d97c6f5ef9 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f721c63e9481908635d354b164c4db completed May 3, 2026, 10:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a374115c1b88190998583fee269e7e6 completed June 21, 2026, 1:40 a.m.
NEDg Description generation batch_6a3741a10b488190a59ffb0888878bd8 completed June 21, 2026, 1:42 a.m.
NED2 Entity disambiguation (via description) batch_6a3743223b3881909db5005278415166 completed June 21, 2026, 1:49 a.m.
Created at: May 1, 2026, 2:03 a.m.