Triple
T9452124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Ken Park |
E227917
|
entity |
| Predicate | basedOnAspect |
P88258
|
FINISHED |
| Object | Ken Jeong’s real-life medical background |
—
|
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: Ken Jeong’s real-life medical background | Statement: [Dr. Ken Park, basedOnAspect, Ken Jeong’s real-life medical background]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnAspect Context triple: [Dr. Ken Park, basedOnAspect, Ken Jeong’s real-life medical background]
-
A.
onAspect
Indicates that one entity is positioned on a particular side, surface, or facet of another entity.
-
B.
basedOnBy
Indicates that one entity is derived from, justified by, or constructed using another entity as its source, foundation, or reference.
-
C.
areBasedOn
Indicates that one entity is founded, derived, or developed from the principles, content, or structure of another entity.
-
D.
basedOnComposition
Indicates that one entity is determined, derived, or defined according to the constituent parts or composition of another entity.
-
E.
coversAspect
Indicates that one entity addresses, includes, or deals with a particular aspect or facet of another entity or topic.
- 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_69ca8439f8bc8190997f2ef40c9f0bc2 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f68f9b081908bee041d4fc77e57 |
completed | April 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69cca5596ffc819097e9c8eefd4ef9b8 |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69cca89d0f0c8190b4528990fe708fca |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:52 p.m.