Triple
T36837174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Libro de signos |
E910302
|
entity |
| Predicate | inLiteraryCanonOf |
P15594
|
FINISHED |
| Object | Colombia |
—
|
NE NERFINISHED |
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: Colombia | Statement: [Libro de signos, inLiteraryCanonOf, Colombia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inLiteraryCanonOf Context triple: [Libro de signos, inLiteraryCanonOf, Colombia]
-
A.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
-
B.
hasLiterarySignificance
chosen
Indicates that something holds notable importance, influence, or value within the realm of literature or literary studies.
-
C.
literaryCollection
Indicates that one entity is a collection or compilation of literary works that includes or is associated with the other entity.
-
D.
usesLiteraryLens
Indicates that one entity analyzes, interprets, or evaluates another entity (such as a text or work) through a specific literary lens or critical framework.
-
E.
hasLiteraryStandard
Indicates that one entity defines, specifies, or embodies the accepted literary norm or standard used by 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_69f76e7e9d60819092442fba73290a46 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fd6834cc8190aa27153d6a99f3bb |
completed | May 5, 2026, 2:23 p.m. |
| PD | Predicate disambiguation | batch_69f7cf7890008190a8bc355ff2d61c86 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:13 p.m.