Triple
T216551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Thomas |
E4116
|
entity |
| Predicate | immigrationStatus |
P7852
|
FINISHED |
| Object | part of U.S. customs and immigration area |
—
|
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: part of U.S. customs and immigration area | Statement: [Saint Thomas, immigrationStatus, part of U.S. customs and immigration area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: immigrationStatus Context triple: [Saint Thomas, immigrationStatus, part of U.S. customs and immigration area]
-
A.
yearOfImmigration
Indicates the specific year in which an entity immigrated to a new country or region.
-
B.
acquireCitizenshipBy
Indicates the process or means by which an entity obtains or is granted citizenship through a specific method, action, or legal basis.
-
C.
immigratedTo
Indicates that an entity moved from its country of origin to live permanently in another specified country or region.
-
D.
hasCustomsAndImmigration
chosen
Indicates that customs and immigration control services are present or provided at a given location or facility.
-
E.
countryOfCitizenship
Indicates the country in which a person or entity holds legal 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b52190481908f299d26122bafd2 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.