Triple
T33509502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Major Frank Burns |
E858200
|
entity |
| Predicate | attitudeTowardRegulations |
P177049
|
FINISHED |
| Object | strict |
—
|
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: strict | Statement: [Major Frank Burns, attitudeTowardRegulations, strict]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: attitudeTowardRegulations Context triple: [Major Frank Burns, attitudeTowardRegulations, strict]
-
A.
allowsRegulationOf
Indicates that one entity grants the authority, means, or conditions for another entity to control, manage, or govern something.
-
B.
hasRegulations
Indicates that one entity imposes, contains, or is associated with rules or regulatory requirements that govern the behavior or operation of another entity.
-
C.
supportsRegulation
Indicates that one entity endorses, backs, or advocates for the implementation or continuation of a specific regulation.
-
D.
attitudeTowardSocialNorms
Indicates an entity’s stance, feelings, or orientation regarding prevailing social rules, expectations, or norms.
-
E.
regulationImpact
Indicates how a regulation influences, constrains, or alters the behavior, performance, or outcomes associated with the related entities.
- F. None of above. chosen
Provenance (4 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_69f3497721848190978fbee5e0a526f8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f85bfba48190aba95b40642a8ca7 |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f814fcf48190ae4504154d1b2c05 |
completed | May 3, 2026, 7:24 a.m. |
Created at: May 1, 2026, 1:38 a.m.