Triple
T5915713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mirandés |
E131573
|
entity |
| Predicate | hasOrthographyStandardizedInYear |
P7432
|
FINISHED |
| Object | 1999 |
—
|
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: 1999 | Statement: [Mirandés, hasOrthographyStandardizedInYear, 1999]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOrthographyStandardizedInYear Context triple: [Mirandés, hasOrthographyStandardizedInYear, 1999]
-
A.
hasStandardOrthographySince
chosen
Indicates that a language or writing system has used a particular standardized orthography starting from a specified point in time.
-
B.
wasGraduallyStandardizedIn
Indicates that something became standardized or uniform within a particular context through a gradual, step-by-step process over time.
-
C.
usesStandardOrthographyOf
Indicates that one entity writes or represents language according to the standard orthographic system defined for another entity.
-
D.
hasOfficialOrthography
Indicates that an entity has a formally recognized and standardized system for writing its language or name.
-
E.
shareHistoricalLanguageStandardization
Indicates that the entities have undergone or adopted the same process or system of historical language standardization.
- 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c048fc112c8190b905bf561c9de096 |
completed | March 22, 2026, 7:54 p.m. |
| PD | Predicate disambiguation | batch_69c03352208c8190968efed05a9fd416 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.