Triple
T10184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Food Administrator |
E206
|
entity |
| Predicate | hasPolicy |
P172
|
FINISHED |
| Object | promotion of meatless days |
—
|
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: promotion of meatless days | Statement: [United States Food Administrator, hasPolicy, promotion of meatless days]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPolicy Context triple: [United States Food Administrator, hasPolicy, promotion of meatless days]
-
A.
implementedPolicy
chosen
Indicates that a particular policy has been put into effect or carried out by an entity.
-
B.
admissionPolicy
Indicates the rules or criteria governing whether and how entities are allowed to be admitted or granted access.
-
C.
hasRole
Indicates that an entity occupies, performs, or is assigned a specific role or function in relation to another entity or context.
-
D.
hasGenderPolicy
Indicates that an entity has adopted, implemented, or is governed by a specific policy related to gender issues or gender equality.
-
E.
hasScope
Indicates that one entity defines, limits, or encompasses the range, extent, or applicability within which another entity operates or is valid.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a240b249788190af8dbf7e80e9c91b |
completed | Feb. 28, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69a23fe52ec48190a4d24101c91434ed |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.