Triple
T4240680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ayin |
E95403
|
entity |
| Predicate | hasStrokeCountApproximate |
P44929
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Ayin, hasStrokeCountApproximate, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStrokeCountApproximate Context triple: [Ayin, hasStrokeCountApproximate, 1]
-
A.
hasStrokeCountApprox
chosen
Indicates an approximate number of strokes associated with writing or drawing the related entity.
-
B.
hasStrokeOrder
Indicates that there is a specific, ordered sequence of strokes used to write or draw the related symbol or character.
-
C.
hasTraditionalCharacter
Indicates that an entity is associated with or represented by a traditional (non-simplified or historically established) written character form.
-
D.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
E.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
- 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_69b3453d91548190b4d4ef8fe52aa2ac |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e87c9d88190a093b1289df5b974 |
completed | March 12, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69b347f587148190a1830503459939b6 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:05 p.m.