Triple
T420668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael I. Pupin |
E8093
|
entity |
| Predicate | citizenshipAcquired |
P4308
|
FINISHED |
| Object | naturalized citizen of the United States |
—
|
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: naturalized citizen of the United States | Statement: [Michael I. Pupin, citizenshipAcquired, naturalized citizen of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: citizenshipAcquired Context triple: [Michael I. Pupin, citizenshipAcquired, naturalized citizen of the United States]
-
A.
acquireCitizenshipBy
chosen
Indicates the process or means by which an entity obtains or is granted citizenship through a specific method, action, or legal basis.
-
B.
citizenshipGrantedYear
Indicates the specific year in which an entity was officially granted citizenship.
-
C.
laterCitizenship
Indicates that an entity acquired citizenship in a country or polity at a later point in time, after some earlier status or affiliation.
-
D.
countryOfCitizenship
Indicates the country in which a person or entity holds legal citizenship.
-
E.
namedAfterCountryOfCitizenship
Indicates that something is named after the country where a person holds citizenship.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebf65cc81908196fee4f09d5889 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd3b948819097d96c73d0a0f699 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.