Triple
T20630194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Cuban Stars |
E506932
|
entity |
| Predicate | languageOfTeamCulture |
P101585
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [New York Cuban Stars, languageOfTeamCulture, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfTeamCulture Context triple: [New York Cuban Stars, languageOfTeamCulture, Spanish]
-
A.
languageOfTeamEnvironment
Indicates the primary language used for communication and collaboration within a team’s working environment.
-
B.
languageOfTeamCountry
Indicates that a particular language is the official or primary language used in the country to which a given team belongs.
-
C.
languageAndCultureDimension
Indicates a relationship where aspects of language are connected to, shaped by, or reflective of broader cultural dimensions or contexts.
-
D.
languageOfMembers
chosen
Indicates that the specified language is used or spoken by the members of a given group or organization.
-
E.
languageOfClubHeritage
Indicates the language traditionally associated with or used to represent a club’s cultural or historical heritage.
- 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_69e0b4bd4a0081908d4e97a590a33fb2 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6ad09c71881909698d3c2576cc181 |
completed | April 20, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69e5a0155bd48190b3c769a12cc2c83d |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 16, 2026, 11:42 a.m.