Triple
T14910617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tammy Suzanne Green Baldwin |
E371250
|
entity |
| Predicate | senatorialRank |
P14818
|
FINISHED |
| Object | senior United States senator |
—
|
LITERAL 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: senior United States senator | Statement: [Tammy Suzanne Green Baldwin, senatorialRank, senior United States senator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: senatorialRank Context triple: [Tammy Suzanne Green Baldwin, senatorialRank, senior United States senator]
-
A.
electoralRank
Indicates the position or level an entity holds within an electoral hierarchy or ranking system.
-
B.
rankInStateCouncil
Indicates the position or level an individual holds within a specific state's council hierarchy.
-
C.
numberOfSenates
Indicates the total count of senate bodies associated with or present in a given context or entity.
-
D.
senateMembership
chosen
Indicates that an entity holds or has held a position as a member of a senate.
-
E.
numberAppointedBySenate
Indicates the number of individuals who were appointed to a position or role through the formal approval or decision of a senate.
- F. None of above.
Provenance (3 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61c6b9c8190a92934d49b98fe46 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:26 a.m.