Triple
T20599955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frenchtown, Michigan |
E506147
|
entity |
| Predicate | originalLanguageOfSettlers |
P82461
|
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: [Frenchtown, Michigan, originalLanguageOfSettlers, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalLanguageOfSettlers Context triple: [Frenchtown, Michigan, originalLanguageOfSettlers, French]
-
A.
languageOfColonialSettlers
chosen
Indicates the language historically spoken by colonial settlers in a given place or context.
-
B.
languageOfFounders
Indicates the language or languages spoken or used by the founders of an entity.
-
C.
originOfSettlement
Indicates the place, source, or starting point from which a settlement was established or originated.
-
D.
languageOfAdoption
Indicates the language in which an adoption (e.g., of a text, standard, or practice) is formally made or recorded.
-
E.
locatedInSettlementFoundedBy
Indicates that something is located in a settlement that was founded by a specified entity.
- 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa1ef9ac8190b05e23c149529cb9 |
completed | April 20, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69e59fffe1748190825e4eaa90340631 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:41 a.m.