Triple
T7803311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atlético de Kolkata |
E180483
|
entity |
| Predicate | secondaryLanguageOfFanbase |
P27353
|
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: [Atlético de Kolkata, secondaryLanguageOfFanbase, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryLanguageOfFanbase Context triple: [Atlético de Kolkata, secondaryLanguageOfFanbase, English]
-
A.
languageOfFanbase
Indicates the primary language or languages commonly used by a fanbase in its communication and expression.
-
B.
primaryLanguageSide2
Indicates that the second entity in the relationship uses or is associated with the primary language specified.
-
C.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
D.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
E.
secondLanguageSpeakers
chosen
Indicates that the referenced language is spoken as a second (non-native) language by the specified group or number of people.
- 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_69ca827e50cc8190a92a733577184938 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:34 p.m.