Triple
T20599934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frenchtown, Michigan |
E506147
|
entity |
| Predicate | earlySettlers |
P140721
|
FINISHED |
| Object | French Canadians |
—
|
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 Canadians | Statement: [Frenchtown, Michigan, earlySettlers, French Canadians]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: earlySettlers Context triple: [Frenchtown, Michigan, earlySettlers, French Canadians]
-
A.
firstSettlersArrived
Indicates that the earliest group of settlers reached or came to a particular place or region.
-
B.
firstPermanentSettler
Indicates that the subject is the earliest individual or group to establish a lasting, continuous residence in the location or entity specified by the object.
-
C.
regionBeforeColonization
Indicates that a region is characterized or described as it existed prior to colonization by an external power.
-
D.
hasNotableEarlySettler
Indicates that an entity is associated with a historically significant person who was among its earliest settlers.
-
E.
firstEuropeanSettlementOn
Indicates that one entity is the first European-established settlement located on the specified place or territory.
- 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_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. |
| PDg | Predicate description generation | batch_69e5a6a9f3f88190b961db9aca36f7da |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 16, 2026, 11:40 a.m.