Triple
T1684319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Micromégas |
E36407
|
entity |
| Predicate | usesCharactersAs |
P32725
|
FINISHED |
| Object | allegorical figures |
—
|
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: allegorical figures | Statement: [Micromégas, usesCharactersAs, allegorical figures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCharactersAs Context triple: [Micromégas, usesCharactersAs, allegorical figures]
-
A.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
-
B.
usesCharacterSet
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
-
C.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
D.
basedOnCharacterBy
Indicates that one work, adaptation, or portrayal is derived from or inspired by a character created by another entity.
-
E.
usesAlphabet
Indicates that one entity employs or is written using the alphabet or writing system associated with another entity.
- F. None of above. chosen
Provenance (4 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aba644070c81908745b56d981fe273 |
completed | March 7, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69aa61b57a6881909373af287ef24799 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69aba641e6a881909645577e72b53df2 |
completed | March 7, 2026, 4:14 a.m. |
Created at: March 4, 2026, 7:29 p.m.