Triple
T885222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denis McDonough |
E19114
|
entity |
| Predicate | hasWorkedOnPolicyArea |
P5037
|
FINISHED |
| Object | health care for veterans |
—
|
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: health care for veterans | Statement: [Denis McDonough, hasWorkedOnPolicyArea, health care for veterans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorkedOnPolicyArea Context triple: [Denis McDonough, hasWorkedOnPolicyArea, health care for veterans]
-
A.
hasPolicyArea
Indicates that an entity (such as a policy, program, or initiative) is associated with or pertains to a specific policy area or domain.
-
B.
coversPolicyArea
chosen
Indicates that a policy, document, or initiative includes or addresses a particular policy area or topic within its scope.
-
C.
hasWorkedIn
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
-
D.
hasWorkedFor
Indicates that an entity has been employed by or has provided work or services to another entity.
-
E.
hasResearchArea
Indicates that an entity (such as a person, project, or organization) is associated with or focused on a particular field or area of research.
- 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ae787bf081909533082ca013624a |
completed | March 1, 2026, 9:24 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8ff8c48190a33b00acf65c1276 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.