Triple
T35730963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US BPT |
E1032749
|
entity |
| Predicate | hasLanguageNeutrality |
P7055
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [US BPT, hasLanguageNeutrality, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageNeutrality Context triple: [US BPT, hasLanguageNeutrality, yes]
-
A.
hasLanguageNeutralForm
Indicates that an entity has a form or representation that is independent of any specific natural language.
-
B.
languageNeutral
chosen
Indicates that the relationship or action is independent of any specific natural language, applying uniformly across different linguistic contexts.
-
C.
isToneNeutral
Indicates that the tone of the referenced content is neither positive nor negative, but emotionally neutral or unbiased.
-
D.
guaranteedNeutralityOf
Indicates that one entity ensures and commits to maintaining impartiality or non-involvement of another entity in a given context or interaction.
-
E.
hasLanguageAspect
Indicates that an entity is associated with a particular linguistic aspect, such as tense, mood, or grammatical feature, in relation to a language.
- 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_69f76e10e59081908d81ad9ce22f40b6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff965f9be48190b015b788207be676 |
completed | May 9, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69ff95d3015c8190b9d293fe31b859c3 |
completed | May 9, 2026, 8:15 p.m. |
Created at: May 3, 2026, 4:05 p.m.