Triple
T24935088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toyotomi Japan |
E623290
|
entity |
| Predicate | unifiedMostOf |
P157254
|
FINISHED |
| Object | Japanese archipelago |
—
|
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 archipelago | Statement: [Toyotomi Japan, unifiedMostOf, Japanese archipelago]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: unifiedMostOf Context triple: [Toyotomi Japan, unifiedMostOf, Japanese archipelago]
-
A.
uniformizedBy
Indicates that one entity has been made uniform, standardized, or brought into a consistent form or structure by another entity.
-
B.
isMostly
Indicates that one entity constitutes the greater part or majority of another entity in amount, extent, or composition.
-
C.
uniformizes
Indicates making multiple entities or elements consistent, standardized, or uniform in form, appearance, or behavior.
-
D.
containsMostOf
Indicates that one entity includes the majority (but not necessarily all) of the substance, elements, or components of another entity.
-
E.
isMost
Indicates that one entity has the greatest degree or quantity of a specified property or attribute compared to all others in a given set.
- F. None of above. chosen
Provenance (4 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f423d4043c8190952356417e9b504c |
completed | May 1, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69f4210130d08190ae30b7943f7a0bbc |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f423637bec8190a1701421ac86a3b7 |
completed | May 1, 2026, 3:52 a.m. |
Created at: April 18, 2026, 5:30 a.m.