Triple
T1433483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muslims |
E30503
|
entity |
| Predicate | dietaryLaw |
P2105
|
FINISHED |
| Object | halal |
—
|
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: halal | Statement: [Muslims, dietaryLaw, halal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dietaryLaw Context triple: [Muslims, dietaryLaw, halal]
-
A.
hasDietaryLaw
chosen
Indicates that one entity prescribes, follows, or is governed by a specific set of dietary rules or restrictions associated with another entity.
-
B.
religiousRestriction
Indicates that one entity imposes, experiences, or is subject to limitations or rules based on religious beliefs or practices in relation to another entity or context.
-
C.
nutritionType
Indicates the specific category or kind of nutritional characteristic or value associated with an entity.
-
D.
diet
Indicates that an entity regularly consumes a particular type or range of food as its primary source of nutrition.
-
E.
containsLawOn
Indicates that one entity (such as a document, code, or regulation) includes or sets forth legal provisions concerning another entity or subject.
- 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_69a498fc69ec8190b61722bd4b67c4d2 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c500a9888190a16fbb1ec97a79c9 |
completed | March 1, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69a4c4771c9481908ae47c959debbe77 |
completed | March 1, 2026, 10:57 p.m. |
Created at: March 1, 2026, 8 p.m.