Triple
T8126043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mamoru |
E189731
|
entity |
| Predicate | hasNameDaySystem |
P33973
|
FINISHED |
| Object | no traditional name day in Japanese culture |
—
|
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: no traditional name day in Japanese culture | Statement: [Mamoru, hasNameDaySystem, no traditional name day in Japanese culture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameDaySystem Context triple: [Mamoru, hasNameDaySystem, no traditional name day in Japanese culture]
-
A.
hasDayNameSystem
chosen
Indicates that an entity employs or is associated with a particular system for naming or designating days.
-
B.
hasNameDay
Indicates that an entity is associated with a specific date on which its name is traditionally celebrated (a name day).
-
C.
hasNameDayRelation
Indicates a relationship where a person’s name is associated with a specific name day or feast day in a calendar.
-
D.
hasNameDayType
Indicates that an entity’s name day is classified under a specific type or category of name day.
-
E.
hasNameDayContext
Indicates that an entity’s name day is interpreted or celebrated within a specific contextual framework (such as culture, calendar system, or tradition).
- 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_69ca82bb74848190afb1f18640632c10 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4c4c2e388190b86854f8b1765e61 |
completed | March 31, 2026, 4:23 a.m. |
| PD | Predicate disambiguation | batch_69cb3696379c8190a20965e59ed8f370 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:34 p.m.