Triple
T1380957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ISO/IEC 8859-1 |
E29335
|
entity |
| Predicate | graphicCharactersCount |
P27160
|
FINISHED |
| Object | 191 |
—
|
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: 191 | Statement: [ISO/IEC 8859-1, graphicCharactersCount, 191]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: graphicCharactersCount Context triple: [ISO/IEC 8859-1, graphicCharactersCount, 191]
-
A.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
B.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
C.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
D.
numberOfCommonUseCharacters
Indicates the count of characters that are shared in common between two entities’ representations or strings.
-
E.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
- 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_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. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.