Triple
T305757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cathedral of College Basketball |
E6294
|
entity |
| Predicate | languageOfNickname |
P15
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Cathedral of College Basketball, languageOfNickname, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfNickname Context triple: [Cathedral of College Basketball, languageOfNickname, English]
-
A.
languageOfWorkOrName
chosen
Indicates the language in which a work is created or a name is expressed.
-
B.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
C.
nameInFrench
Indicates that an entity is known or referred to by a specific name expressed in the French language.
-
D.
eraNameInJapanese
Indicates the Japanese-language name used for a specific historical or calendar era.
-
E.
honorificNickname
Indicates that one entity is referred to by a respectful or honorific nickname by another entity or in a given context.
- 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_69a2e79230508190b912ecb555aae17e |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea11c4908190a8723033bdf6f479 |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e93db11881909b07ba5e76d91feb |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.