Triple
T188973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basic Latin |
E3675
|
entity |
| Predicate | containsCharacterCategory |
P1445
|
FINISHED |
| Object | uppercase Latin letters |
—
|
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: uppercase Latin letters | Statement: [Basic Latin, containsCharacterCategory, uppercase Latin letters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsCharacterCategory Context triple: [Basic Latin, containsCharacterCategory, uppercase Latin letters]
-
A.
hasSpecialCharacter
Indicates that a given entity (such as a string or identifier) contains at least one non-alphanumeric special character.
-
B.
hasNumberCategory
Indicates that an entity is associated with a specific numerical classification or type.
-
C.
UnicodeBlock
chosen
Indicates that a character belongs to a specific contiguous range of code points defined as a Unicode block.
-
D.
characterizedBy
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
E.
usesDiacritics
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
- 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a2594abeec8190a48f36817e647fcd |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25672332081909386f35f3ca15dd2 |
completed | Feb. 28, 2026, 2:44 a.m. |
Created at: Feb. 28, 2026, 2:41 a.m.