Triple
T21985042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangul Syllables |
E542934
|
entity |
| Predicate | hasTotalAssignedCharacters |
P32078
|
FINISHED |
| Object | 11172 |
—
|
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: 11172 | Statement: [Hangul Syllables, hasTotalAssignedCharacters, 11172]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalAssignedCharacters Context triple: [Hangul Syllables, hasTotalAssignedCharacters, 11172]
-
A.
containsAssignedCharacters
Indicates that an entity includes or holds one or more characters that have been specifically assigned to it.
-
B.
numberOfCharacters
chosen
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
C.
hasCharacters
Indicates that an entity (such as a work or story) includes or features certain characters as part of its content.
-
D.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
E.
hasTotalNumber
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
- 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_69e0c48136b081908831fa907cc02e18 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12708cdcc81909511d9f81bd8f20e |
completed | April 28, 2026, 9:30 p.m. |
| PD | Predicate disambiguation | batch_69e6f6154e408190acc5b2c278acaff4 |
completed | April 21, 2026, 3:59 a.m. |
Created at: April 16, 2026, 8:04 p.m.