Triple
T33278162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jan Michalski Prize for Literature |
E851959
|
entity |
| Predicate | languagesAccepted |
P73062
|
FINISHED |
| Object | all languages |
—
|
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: all languages | Statement: [Jan Michalski Prize for Literature, languagesAccepted, all languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languagesAccepted Context triple: [Jan Michalski Prize for Literature, languagesAccepted, all languages]
-
A.
eligibleLanguage
chosen
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
B.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
C.
languageTargets
Indicates that a language is specifically directed at, intended for, or used to address a particular target entity (such as an audience, system, or domain).
-
D.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
E.
languagePair
Indicates a relationship that associates two specific languages as a paired combination, typically for translation, comparison, or mapping between them.
- 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_69f349653da08190819876015a298fdb |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a00144238708190acbec3f791cc873e |
completed | May 10, 2026, 5:14 a.m. |
| PD | Predicate disambiguation | batch_6a00120244a4819090ef39070aba9d99 |
completed | May 10, 2026, 5:05 a.m. |
Created at: May 1, 2026, 1:32 a.m.