Triple
T21255253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jim Harris |
E523850
|
entity |
| Predicate | isAmbiguousWith |
P2289
|
FINISHED |
| Object | Jim Harris (businessperson) |
—
|
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: Jim Harris (businessperson) | Statement: [Jim Harris, isAmbiguousWith, Jim Harris (businessperson)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jim Harris (businessperson) Context triple: [Jim Harris, isAmbiguousWith, Jim Harris (businessperson)]
-
A.
Larry Harris
Larry Harris was a music industry executive best known as a co-founder and key architect of the influential 1970s label Casablanca Records.
-
B.
Jeff Harris
Jeff Harris was an American television writer and producer best known for co-creating the popular sitcom "Diff'rent Strokes."
-
C.
Jim Harris
Jim Harris is a relatively common personal name shared by multiple individuals across fields such as politics, sports, and the arts.
-
D.
Jim Harris
chosen
Jim Harris is a technology executive best known as one of the founders of the computer company Compaq.
-
E.
Jack Harris
Jack Harris is an actor known for his role in the film "Crashing Towers."
- 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_69e0b5146c108190adc9adb73e90abff |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e735a0e7dc8190b591b5b6786ce619 |
completed | April 21, 2026, 8:30 a.m. |
Created at: April 16, 2026, 3:58 p.m.