Triple
T8774203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Amsterdam |
E208536
|
entity |
| Predicate | builderNationality |
P85326
|
FINISHED |
| Object | Dutch |
—
|
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: Dutch | Statement: [Fort Amsterdam, builderNationality, Dutch]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: builderNationality Context triple: [Fort Amsterdam, builderNationality, Dutch]
-
A.
bearerNationality
Indicates that one entity is the country or nationality associated with the bearer of another entity, such as a document or credential.
-
B.
brandNationality
Indicates that a brand is associated with or originates from a particular country or nationality.
-
C.
nationalityInText
Indicates that a person's nationality is mentioned or specified within a given text.
-
D.
namedForNationality
Indicates that something is named after or in reference to a particular nationality or national identity.
-
E.
nationalityOfPersonReferredTo
Indicates that one entity is the country or nationality associated with the person referenced by the other entity.
- 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_69ca835edb4481909b4aafb616dc5eb7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f2ef3288190988bd69e8a02e741 |
completed | March 31, 2026, 11:56 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1aff3881908be6a9cbc9f50461 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cfddef48190aee764ee7b25bae9 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:41 p.m.