Triple
T30067888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stormi Webster |
E764085
|
entity |
| Predicate | languageOfSocialMediaContent |
P31857
|
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: [Stormi Webster, languageOfSocialMediaContent, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfSocialMediaContent Context triple: [Stormi Webster, languageOfSocialMediaContent, English]
-
A.
languageOfSocialSphere
Indicates the language predominantly used within a particular social group, community, or social context.
-
B.
languageOfMostTweets
Indicates the primary language in which the majority of a user's tweets are written.
-
C.
mediaLanguage
Indicates the language in which a media item (such as a film, broadcast, or publication) is originally produced or presented.
-
D.
contentLanguage
chosen
Indicates the language in which the content is expressed or intended to be understood.
-
E.
languageOfSurroundingCulture
Indicates that one entity is the language predominantly used or characteristic of the surrounding culture associated with another entity.
- 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_69f2247221388190a13a22c47094a0ef |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fef2db323c8190821bda53f22a42be |
completed | May 9, 2026, 8:39 a.m. |
| PD | Predicate disambiguation | batch_69fef21d63c88190abf6a99b59b3c655 |
completed | May 9, 2026, 8:36 a.m. |
Created at: April 29, 2026, 7 p.m.