Triple
T9953665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krusty Burger |
E195391
|
entity |
| Predicate | hasHealthInspectionIssue |
P37988
|
FINISHED |
| Object | poor hygiene |
—
|
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: poor hygiene | Statement: [Krusty Burger, hasHealthInspectionIssue, poor hygiene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthInspectionIssue Context triple: [Krusty Burger, hasHealthInspectionIssue, poor hygiene]
-
A.
hasNoIssue
Indicates that there are no problems, defects, or conflicts associated with the referenced entity or situation.
-
B.
hasPracticeIssue
chosen
Indicates that an entity is associated with a specific problem, concern, or challenge arising in practical or real-world practice.
-
C.
hadIssue
Indicates that an entity experienced, encountered, or was affected by a particular problem, defect, or difficulty.
-
D.
hasInternalIssue
Indicates that an entity is experiencing a problem, fault, or malfunction originating within itself or its internal components or processes.
-
E.
hasHealthWarningOnPackaging
Indicates that an item’s packaging displays a health-related warning message or symbol.
- 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_69ca82eaaa008190a54fa1a9f954b9ad |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb694b95481909d049302818e7137 |
completed | April 2, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69cd1d97c44081908730071269f07712 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:46 p.m.