Triple
T35728060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hana and Alice |
E1032669
|
entity |
| Predicate | hasOfficialTitleInJapanese |
P9882
|
FINISHED |
| Object | 花とアリス |
—
|
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: 花とアリス | Statement: [Hana and Alice, hasOfficialTitleInJapanese, 花とアリス]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOfficialTitleInJapanese Context triple: [Hana and Alice, hasOfficialTitleInJapanese, 花とアリス]
-
A.
hasOfficialNameInJapanese
chosen
Indicates that an entity has an official, formally recognized name expressed in the Japanese language.
-
B.
hasNameInJapanese
Indicates that an entity is associated with a specific name expressed in the Japanese language.
-
C.
honorificTitleInJapanese
Indicates that one entity is referred to using a specific honorific title or suffix in the Japanese language in relation to another entity.
-
D.
officeHolderTitleInJapanese
Indicates the official title or designation of an office holder as expressed in the Japanese language.
-
E.
equivalentTitleInJapanese
Indicates that one entity has a corresponding or matching title in Japanese that is equivalent in meaning or usage to the other entity’s title.
- 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_69f76e102b5881909e5d63a30a5cecbe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fbc9d1dba881908c399b8e1dc13ce2 |
completed | May 6, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69fbc8ec03ac8190a757563f96fab283 |
completed | May 6, 2026, 11:04 p.m. |
Created at: May 3, 2026, 4:05 p.m.