Triple
T37701925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tono |
E939085
|
entity |
| Predicate | spellingStandard |
P81382
|
FINISHED |
| Object | does not conform to standard Spanish orthography for Toño |
—
|
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: does not conform to standard Spanish orthography for Toño | Statement: [Tono, spellingStandard, does not conform to standard Spanish orthography for Toño]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spellingStandard Context triple: [Tono, spellingStandard, does not conform to standard Spanish orthography for Toño]
-
A.
spellingStyle
Indicates the particular orthographic convention or system of spelling that is used or preferred in a given context.
-
B.
spellingStatus
chosen
Indicates the correctness or condition of the spelling of a given text or term.
-
C.
usesStandardOrthographyOf
Indicates that one entity writes or represents language according to the standard orthographic system defined for another entity.
-
D.
spellingStability
Indicates the degree to which the spelling of a word or term remains consistent over time or across different uses.
-
E.
hasTypicalSpelling
Indicates that one form is the standard or commonly accepted spelling of another form.
- 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_69f76eda6ae48190b3111071eeacc038 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb084760c8190a1554985d3c3cb7a |
completed | May 6, 2026, 9:20 p.m. |
| PD | Predicate disambiguation | batch_69fbadf3cb548190ba3b7514f76b790a |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:18 p.m.