Triple
T38345103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orange Days |
E1041516
|
entity |
| Predicate | hasSignLanguageElement |
P88340
|
FINISHED |
| Object | Japanese Sign Language featured in story |
—
|
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 Sign Language featured in story | Statement: [Orange Days, hasSignLanguageElement, Japanese Sign Language featured in story]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignLanguageElement Context triple: [Orange Days, hasSignLanguageElement, Japanese Sign Language featured in story]
-
A.
hasAdditionalLanguageOfSignage
Indicates that an entity has signage presented in one or more additional languages beyond the primary language used.
-
B.
usesSignLanguage
chosen
Indicates that one entity communicates using sign language with or in relation to another entity.
-
C.
languageOfSignage
Indicates the language used on signs or written displays associated with an entity.
-
D.
recognizedSignLanguage
Indicates that one entity has correctly identified or understood a sign language used or produced by another entity.
-
E.
callSignLanguage
Indicates that one entity communicates with another using sign language as the medium of the call or conversation.
- 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_69f76e2ad95481908c920c0e5c1c3e26 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe9fb9735c8190a360b556c9d00b3f |
completed | May 9, 2026, 2:45 a.m. |
| PD | Predicate disambiguation | batch_69fe9eaa88008190a9b2a469dc685002 |
completed | May 9, 2026, 2:40 a.m. |
Created at: May 3, 2026, 4:30 p.m.