Triple
T15740320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2513 character generator ROM |
E381584
|
entity |
| Predicate | intendedCharacters |
P32725
|
FINISHED |
| Object | 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: letters | Statement: [2513 character generator ROM, intendedCharacters, letters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedCharacters Context triple: [2513 character generator ROM, intendedCharacters, letters]
-
A.
usesCharactersAs
chosen
Indicates that one entity employs or incorporates specific characters (such as letters, symbols, or glyphs) from another entity for its representation or functioning.
-
B.
hasCharacters
Indicates that an entity (such as a work or story) includes or features certain characters as part of its content.
-
C.
targetsCharacter
Indicates that one entity is the intended focus or target of another entity’s action, effect, or behavior.
-
D.
representsForCharacters
Indicates that one entity performs a representation or advocacy role on behalf of specific characters.
-
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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:46 a.m.