Triple
T1380956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ISO/IEC 8859-1 |
E29335
|
entity |
| Predicate | codePointsCount |
P24670
|
FINISHED |
| Object | 256 |
—
|
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: 256 | Statement: [ISO/IEC 8859-1, codePointsCount, 256]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: codePointsCount Context triple: [ISO/IEC 8859-1, codePointsCount, 256]
-
A.
maximumCodePoints
chosen
Indicates the maximum number of Unicode code points that are allowed or supported in a given context or value.
-
B.
DELCodePoint
Indicates a relationship where a specific code point corresponds to or is designated as the delete (DEL) control character in a character encoding system.
-
C.
hasCombiningMarks
Indicates that an entity (such as a character or string) includes one or more combining marks attached to a base element.
-
D.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
E.
numberOfCommonUseCharacters
Indicates the count of characters that are shared in common between two entities’ representations or strings.
- 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c31b176c8190a896183140c5c8be |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befe343c81909f758440a531b5be |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.