Triple
T15327623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Occasu |
E366451
|
entity |
| Predicate | hasMacronizedForm |
P93847
|
FINISHED |
| Object | occāsū |
—
|
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: occāsū | Statement: [Occasu, hasMacronizedForm, occāsū]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMacronizedForm Context triple: [Occasu, hasMacronizedForm, occāsū]
-
A.
hasMacronRomanization
chosen
Indicates that an entity is associated with a Romanized form of text that uses macrons to mark long vowels.
-
B.
accentedFormOf
Indicates that one linguistic form is an accented or diacritically marked variant of another, more basic form.
-
C.
hasContextualLetterForms
Indicates that the written form of a letter changes shape depending on its surrounding characters or position within a word.
-
D.
hasAccent
Indicates that an entity speaks with or possesses a particular accent or distinctive pronunciation style.
-
E.
usesToneMarks
Indicates that one entity applies or includes diacritical tone marks in the representation or transcription of another entity (such as text, language, or symbols).
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dffd6f88190a0f031ee90c6a7d2 |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca9659f48190b8661df223ce5078 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:16 a.m.