Triple
T9078699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Columbus Park |
E217555
|
entity |
| Predicate | designer |
P184
|
FINISHED |
| Object | Jens Jensen |
E268317
|
NE 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: Jens Jensen | Statement: [Columbus Park, designer, Jens Jensen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jens Jensen Context triple: [Columbus Park, designer, Jens Jensen]
-
A.
Jens Jensen
chosen
Jens Jensen was a prominent Danish-American landscape architect known for his naturalistic designs and influential work on parks and estates in the American Midwest.
-
B.
Jon Jensen
Jon Jensen is the central protagonist of the film "The Salvation," around whom the story’s dramatic events and conflicts revolve.
-
C.
Niels Jensen
Niels Jensen is a software entrepreneur best known as one of the founders of the software company Borland.
-
D.
Jens Grede
Jens Grede is a fashion entrepreneur and co-founder of the shapewear and apparel brand SKIMS, known for building high-profile celebrity-backed labels.
-
E.
Carl Kjeldsberg
Carl Kjeldsberg is a pathologist and academic leader best known as a co-founder of ARUP Laboratories, a major national clinical and anatomic pathology reference laboratory.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca83d6c14c8190bc056d927f00a2a2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc95c93ee48190842623b57e50f4cf |
completed | April 1, 2026, 3:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cffe190c28819098ee017c75d72ad0 |
completed | April 3, 2026, 5:51 p.m. |
Created at: March 30, 2026, 7:12 p.m.