Triple
T250527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baybayin |
E5135
|
entity |
| Predicate | hasApproximateNumberOfCharacters |
P7444
|
FINISHED |
| Object | 17 basic consonant characters |
—
|
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: 17 basic consonant characters | Statement: [Baybayin, hasApproximateNumberOfCharacters, 17 basic consonant characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfCharacters Context triple: [Baybayin, hasApproximateNumberOfCharacters, 17 basic consonant characters]
-
A.
hasApproximateNumberOfLetters
chosen
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
B.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
C.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
D.
numberOfCommonUseCharacters
Indicates the count of characters that are shared in common between two entities’ representations or strings.
-
E.
hasStandardLetterCount
Indicates that an entity’s associated text or label contains a number of letters that matches a predefined standard or expected count.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d38aba8819081d0958eb60ce27e |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b665f8c8190aac6fcbba2a0eebb |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.