Triple
T4437699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ex parte Endo |
E95690
|
entity |
| Predicate | petitionerCitizenship |
P2
|
FINISHED |
| Object | United States 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: United States citizen | Statement: [Ex parte Endo, petitionerCitizenship, United States citizen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: petitionerCitizenship Context triple: [Ex parte Endo, petitionerCitizenship, United States citizen]
-
A.
countryOfCitizenship
chosen
Indicates the country in which a person or entity holds legal citizenship.
-
B.
cityOfCitizenship
Indicates the city in which a person holds legal citizenship or official civic affiliation.
-
C.
citizenshipType
Indicates the specific legal category or status of an individual's citizenship in relation to a state or country.
-
D.
definedCitizenship
Indicates that a formal citizenship status has been legally established or specified for an entity.
-
E.
continentOfCitizenship
Indicates the continent on which a person’s country of citizenship is located.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3558c75948190875a5fb10eac0597 |
completed | March 13, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69b34f6078cc8190831b89f404198cc5 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:31 p.m.