Triple
T15120968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mako |
E361169
|
entity |
| Predicate | citizenshipStatusAfterMarriage |
P117411
|
FINISHED |
| Object | remained a Japanese citizen |
—
|
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: remained a Japanese citizen | Statement: [Mako, citizenshipStatusAfterMarriage, remained a Japanese citizen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: citizenshipStatusAfterMarriage Context triple: [Mako, citizenshipStatusAfterMarriage, remained a Japanese citizen]
-
A.
socialStatusAfterMarriage
Indicates the social status an individual holds as a result of, or following, their marriage.
-
B.
hasMaritalStatusAfterFirstMarriage
Indicates that an entity’s marital status at a given time is the one it holds after its first marriage has occurred.
-
C.
spouseStatusAtMarriage
Indicates the marital status each partner held at the time their marriage to one another was formed.
-
D.
dualCitizenshipStatus
Indicates that an entity holds legal citizenship in two different countries simultaneously.
-
E.
spouseCountryOfCitizenship
Indicates the country in which a person's spouse holds legal citizenship.
- 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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0059f69a881909929a037a0eef702 |
completed | April 15, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69deb96c1d9c81909351558ed97bc5b7 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:06 a.m.