Triple
T1607998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rikichi Tsukada |
E34549
|
entity |
| Predicate | typeOfPerson |
P11881
|
FINISHED |
| Object | career military officer |
—
|
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: career military officer | Statement: [Rikichi Tsukada, typeOfPerson, career military officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfPerson Context triple: [Rikichi Tsukada, typeOfPerson, career military officer]
-
A.
personnelType
chosen
Indicates the classification or role category assigned to a person within an organization or system.
-
B.
typeOfRole
Indicates that one entity specifies the kind or category of role that another entity holds or performs.
-
C.
participantType
Indicates the specific role or category that a participant has within a given event, activity, or relationship.
-
D.
typicalRole
Indicates that one entity serves as the usual, characteristic, or commonly expected role or function of another entity.
-
E.
hasPerson
Indicates that an entity is associated with or includes a specific person.
- 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_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93fa6926081908bc78d15c0be3185 |
completed | March 5, 2026, 8:32 a.m. |
| PD | Predicate disambiguation | batch_69a907c35f848190a2428c52e81d013e |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.