Triple
T23284147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | No-Limit Texas Hold'em |
E588942
|
entity |
| Predicate | isLegalStatusVariesBy |
P39644
|
FINISHED |
| Object | jurisdiction |
—
|
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: jurisdiction | Statement: [No-Limit Texas Hold'em, isLegalStatusVariesBy, jurisdiction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLegalStatusVariesBy Context triple: [No-Limit Texas Hold'em, isLegalStatusVariesBy, jurisdiction]
-
A.
legalStatusVariesBy
chosen
Indicates that the legal status of something differs depending on a specified jurisdiction, context, or set of conditions.
-
B.
hasLegalStatus
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
C.
usedLegalStatus
Indicates that one entity applies or relies on the legal status or classification of another entity in a given context.
-
D.
legalStatusAccordingToIndia
Indicates the legal status or classification of an entity as defined specifically by the laws and regulations of India.
-
E.
hasHighestLegalStatusWithinCountry
Indicates that an entity holds the topmost legally recognized status or rank within a specific country, above all other comparable statuses.
- 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_69e25d16e2c08190a291de254703129e |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f196454b4c8190a797537ce8912241 |
completed | April 29, 2026, 5:25 a.m. |
| PD | Predicate disambiguation | batch_69effcecabd88190856fb6e1d993e4dd |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:58 p.m.