Triple
T28788764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Secretary of State of Washington |
E726892
|
entity |
| Predicate | precededBy |
P97
|
FINISHED |
| Object |
Kim Wyman
Kim Wyman is an American politician and elections administrator who served as the Secretary of State of Washington, overseeing the state’s election systems and public records.
|
E1835417
|
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: Kim Wyman | Statement: [Secretary of State of Washington, precededBy, Kim Wyman]
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: Kim Wyman Triple: [Secretary of State of Washington, precededBy, Kim Wyman]
Generated description
Kim Wyman is an American politician and elections administrator who served as the Secretary of State of Washington, overseeing the state’s election systems and public records.
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_69f0319aabec81908368720196f69a35 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f6587832088190b7857e65ff3a91f4 |
completed | May 2, 2026, 8:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a24bb9b3d2c819093336aa68fafaebc |
completed | June 7, 2026, 12:30 a.m. |
| NEDg | Description generation | batch_6a24bfc8d5f48190897d403ba203f298 |
completed | June 7, 2026, 12:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a24c3ee6bdc8190a0bbf5cb4503d57a |
completed | June 7, 2026, 1:05 a.m. |
Created at: April 28, 2026, 6:22 a.m.