Triple
T5882304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Unicode 5.1 |
E130775
|
entity |
| Predicate | approximateCharacterCount |
P7444
|
FINISHED |
| Object | 100000+ |
—
|
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: 100000+ | Statement: [Unicode 5.1, approximateCharacterCount, 100000+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateCharacterCount Context triple: [Unicode 5.1, approximateCharacterCount, 100000+]
-
A.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
B.
hasApproximateNumberOfLetters
chosen
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
C.
addsCharactersCount
Indicates that one entity increases the number of characters (e.g., text length) in another entity by a specified amount.
-
D.
codePointCount
Indicates the number of Unicode code points contained within a specified range of a character sequence.
-
E.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03fe07b7081909f8577ec3a9a1a8d |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0334bdc308190ad0d7199ab975588 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:57 p.m.