Triple
T29634891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George |
E755683
|
entity |
| Predicate | hasJapaneseNameMeaning |
P125083
|
FINISHED |
| Object | Violet Lightning |
—
|
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: Violet Lightning | Statement: [George, hasJapaneseNameMeaning, Violet Lightning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasJapaneseNameMeaning Context triple: [George, hasJapaneseNameMeaning, Violet Lightning]
-
A.
hasMeaningInJapanese
chosen
Indicates that something (such as a word, phrase, or symbol) possesses a specific meaning when interpreted in the Japanese language.
-
B.
hasNameInJapanese
Indicates that an entity is associated with a specific name expressed in the Japanese language.
-
C.
JapaneseNameOrigin
Indicates that one entity’s name originates from or is derived from the Japanese language or naming tradition in relation to another entity.
-
D.
possibleKanjiMeaning
Indicates that a given meaning is a possible or candidate interpretation associated with a particular kanji character.
-
E.
JapaneseNameReading
Indicates that one entity is the reading or pronunciation (e.g., in kana or romaji) of a Japanese name represented by the other 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_69f0ef88fbe081908f0ad90c1c413f1c |
completed | April 28, 2026, 5:34 p.m. |
| NER | Named-entity recognition | batch_69f66e68f5588190b41a2060d3aea12f |
completed | May 2, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69f66abfdaf08190a55f14c70be6fd4d |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 28, 2026, 6:43 p.m.