Triple
T19890074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quartet (A Model of Decorum and Tranquility) |
E478004
|
entity |
| Predicate | numberOfCharactersFeatured |
P32078
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Quartet (A Model of Decorum and Tranquility), numberOfCharactersFeatured, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCharactersFeatured Context triple: [Quartet (A Model of Decorum and Tranquility), numberOfCharactersFeatured, 4]
-
A.
numberOfCharacters
chosen
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
B.
numberOfPlayableCharacters
Indicates the total count of distinct characters that can be actively controlled or played by a user in a game or interactive experience.
-
C.
totalCharactersInStandard
Indicates the total number of characters defined within a given standard or specification.
-
D.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
E.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
- 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6590ce9f48190a51c0e5ecc828a06 |
completed | April 20, 2026, 4:49 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:52 p.m.