Triple
T31993845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Persian tobacco concession |
E816939
|
entity |
| Predicate | fatwaEffect |
P173100
|
FINISHED |
| Object | nationwide boycott of tobacco |
—
|
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: nationwide boycott of tobacco | Statement: [Persian tobacco concession, fatwaEffect, nationwide boycott of tobacco]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fatwaEffect Context triple: [Persian tobacco concession, fatwaEffect, nationwide boycott of tobacco]
-
A.
fiqh
Indicates the relationship of deriving, applying, or adhering to Islamic legal rulings and jurisprudential judgments regarding actions or situations.
-
B.
hasRulingInFiqh
Indicates that a subject has an associated legal ruling or judgment within the framework of Islamic jurisprudence (fiqh).
-
C.
fiqhScope
Indicates the domain or scope within Islamic jurisprudence (fiqh) to which a particular ruling, issue, or concept applies.
-
D.
positionInIslam
Indicates the specific religious role, rank, or status an entity holds within the context of Islam.
-
E.
الانتماء الفقهي
Indicates the doctrinal or jurisprudential school to which an entity is affiliated or adheres.
- 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_69f348f8002081909a3588758ba94afb |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b3bdbcb08190b9fe7baf11e612a5 |
completed | May 3, 2026, 2:32 a.m. |
| PD | Predicate disambiguation | batch_69f6b151ad008190836c1bcdec503ce2 |
completed | May 3, 2026, 2:22 a.m. |
| PDg | Predicate description generation | batch_69f6b21da77081908c5c015c4606d344 |
completed | May 3, 2026, 2:25 a.m. |
Created at: May 1, 2026, 12:13 a.m.