Triple

T38579389
Position Surface form Disambiguated ID Type / Status
Subject Pierce County government E929497 entity
Predicate hasElectedOffice P1239 FINISHED
Object County Assessor-Treasurer
The County Assessor-Treasurer is a local elected official responsible for valuing property for tax purposes and collecting property taxes within the county.
E2275851 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: County Assessor-Treasurer | Statement: [Pierce County government, hasElectedOffice, County Assessor-Treasurer]
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: County Assessor-Treasurer
Triple: [Pierce County government, hasElectedOffice, County Assessor-Treasurer]
Generated description
The County Assessor-Treasurer is a local elected official responsible for valuing property for tax purposes and collecting property taxes within the county.

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_69f76ebd2248819083978362d81fa35e completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fcd92334a08190811d755487ab28fd completed May 7, 2026, 6:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41ea96fd7c819085c9e9f82adc4b28 completed June 29, 2026, 3:46 a.m.
NEDg Description generation batch_6a41eb116f288190bb2cc9876dea0577 completed June 29, 2026, 3:48 a.m.
NED2 Entity disambiguation (via description) batch_6a41eb7dc3cc8190955444ba736583de completed June 29, 2026, 3:50 a.m.
Created at: May 3, 2026, 4:32 p.m.