Triple
T10259502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French steamship Lotus |
E240557
|
entity |
| Predicate | nationalityOfOfficerOnWatch |
P93121
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [French steamship Lotus, nationalityOfOfficerOnWatch, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalityOfOfficerOnWatch Context triple: [French steamship Lotus, nationalityOfOfficerOnWatch, French]
-
A.
nationalityOfPersonnel
Indicates the country or countries to which the personnel involved in an activity, organization, or context belong by citizenship or national affiliation.
-
B.
commanderNationality
Indicates the national affiliation or citizenship of a given commander.
-
C.
officeHolderNationality
Indicates that the nationality of an office holder is a specified country or nation.
-
D.
bearerNationality
Indicates that one entity is the country or nationality associated with the bearer of another entity, such as a document or credential.
-
E.
operatorNationality
Indicates that an operator has a specific national affiliation or country of origin.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2b5853081909cd0397e08a0f44d |
completed | April 7, 2026, 9:47 a.m. |
| PD | Predicate disambiguation | batch_69d4d1edae6881909a65201b8e51ea0a |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d2b4ae548190b4a4c671f86b82d1 |
completed | April 7, 2026, 9:47 a.m. |
Created at: April 6, 2026, 11:32 a.m.