Triple
T11536393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Facing the Bridge |
E273558
|
entity |
| Predicate | hasOriginalAuthorLanguage |
P74740
|
FINISHED |
| Object | Japanese |
—
|
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: Japanese | Statement: [Facing the Bridge, hasOriginalAuthorLanguage, Japanese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOriginalAuthorLanguage Context triple: [Facing the Bridge, hasOriginalAuthorLanguage, Japanese]
-
A.
originalLanguageAuthor
chosen
Indicates that an author created a work in a particular original language.
-
B.
originalTextLanguage
Indicates the language in which a text was originally written or created before any translation or adaptation.
-
C.
originalLanguageOfWholeWork
Indicates that a given language is the primary or original language in which an entire work (such as a book, film, or other complete creation) was first produced or expressed.
-
D.
indirectOriginLanguage
Indicates that something originates from a particular language, not directly but through one or more intermediate languages or sources.
-
E.
originalPublicationLanguageVariant
Indicates that one language is a specific variant or version of the language in which a work was originally published.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8839b4bb48190b748ec4119f36c11 |
completed | April 10, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69d80879fdb48190be6dacc8aa63c809 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:37 p.m.