Triple
T23401252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rumiyah |
E559508
|
entity |
| Predicate | hasLegalStatusInManyCountries |
P2250
|
FINISHED |
| Object | banned material |
—
|
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: banned material | Statement: [Rumiyah, hasLegalStatusInManyCountries, banned material]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalStatusInManyCountries Context triple: [Rumiyah, hasLegalStatusInManyCountries, banned material]
-
A.
legalStatusInManyCountries
Indicates that the subject has a particular legal classification or standing that is recognized across numerous countries.
-
B.
legalStatusInMostCountries
Indicates the typical legal classification or treatment of something across the majority of countries.
-
C.
hasHighestLegalStatusWithinCountry
Indicates that an entity holds the topmost legally recognized status or rank within a specific country, above all other comparable statuses.
-
D.
hasLegalStatus
chosen
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
E.
legalStatusRecognizedIn
Indicates that an entity’s legal status is officially acknowledged or valid within a specified jurisdiction or legal system.
- 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_69e24549610c8190a069d6411ce5f661 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a4dfa4c881908df0eced4855c0d9 |
completed | April 29, 2026, 6:27 a.m. |
| PD | Predicate disambiguation | batch_69f061ed34288190a2e5e8cae03b0095 |
completed | April 28, 2026, 7:29 a.m. |
Created at: April 17, 2026, 5:37 p.m.