Triple
T19463861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Minnetonka |
E486942
|
entity |
| Predicate | hasCityOnShore |
P969
|
FINISHED |
| Object | Excelsior |
—
|
NE NERFINISHED |
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: Excelsior | Statement: [Lake Minnetonka, hasCityOnShore, Excelsior]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Excelsior Context triple: [Lake Minnetonka, hasCityOnShore, Excelsior]
-
A.
Excelsior
Excelsior is the Latin state motto of New York, meaning "ever upward" and symbolizing aspiration and continual progress.
-
B.
Excelsior
chosen
Excelsior is a small lakeside city in Minnesota known for its historic downtown and location on the shores of Lake Minnetonka.
-
C.
Excelsior
Excelsior is a professional football club from Rotterdam, Netherlands, known as one of Feyenoord’s local city rivals.
-
D.
The Yankee
The Yankee is the nickname of Hank Morgan, the time-traveling 19th-century engineer who becomes a powerful figure in King Arthur’s court in Mark Twain’s novel "A Connecticut Yankee in King Arthur’s Court."
-
E.
The Bell
The Bell is a 1958 novel by British philosopher and author Iris Murdoch that explores morality, religion, and human relationships within a lay religious community.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633cf95988190b13b2153e67d0cac |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.