Triple
T15443761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2012 United States Senate election in Massachusetts |
E369974
|
entity |
| Predicate | turnoutContext |
P7828
|
FINISHED |
| Object | high-profile statewide race |
—
|
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: high-profile statewide race | Statement: [2012 United States Senate election in Massachusetts, turnoutContext, high-profile statewide race]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: turnoutContext Context triple: [2012 United States Senate election in Massachusetts, turnoutContext, high-profile statewide race]
-
A.
turnout
Indicates the number or proportion of participants who attend or take part in an event or activity.
-
B.
electoralContext
chosen
Indicates the relationship between an event or situation and the specific electoral setting (such as an election, campaign, or voting process) in which it occurs or to which it pertains.
-
C.
turnsIn
Indicates that an entity submits or hands over something, typically work or an item, to another party or authority.
-
D.
turningPointIn
Indicates that an event or situation serves as a decisive change or pivotal moment within a larger process, narrative, or development.
-
E.
turnoutVariesByYear
Indicates that the level of turnout changes depending on the specific year considered.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ef666e08190a02a01a676306ab9 |
completed | April 16, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ded28276f481908c2038bb301e57cf |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:21 a.m.