Triple
T11672235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhuyin |
E277409
|
entity |
| Predicate | hasApproximateNumberOfSymbols |
P100700
|
FINISHED |
| Object | 37 base symbols |
—
|
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: 37 base symbols | Statement: [Zhuyin, hasApproximateNumberOfSymbols, 37 base symbols]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfSymbols Context triple: [Zhuyin, hasApproximateNumberOfSymbols, 37 base symbols]
-
A.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
B.
hasApproximateNumberOfAttestedWords
Indicates that an entity is associated with an estimated or approximate count of words that are documented or attested for it.
-
C.
hasApproximateNumberOfPictographs
Indicates that an entity is associated with a quantity of pictographs that is not exact but estimated or approximate.
-
D.
hasApproximateNumberOfLanguages
Indicates that an entity is associated with a quantity representing an estimated or non-exact count of languages.
-
E.
hasApproximateBrickCount
Indicates that an entity is associated with an estimated or non-exact number of bricks.
- F. None of above. chosen
Provenance (4 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a443b6848190a1eb6825fbc49d08 |
completed | April 10, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69d88a77e6e88190b7519100bde76575 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d8938a1f8c81908ffb049fa5fee5a7 |
completed | April 10, 2026, 6:07 a.m. |
Created at: April 8, 2026, 9:40 p.m.