Triple
T5155983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steven Shih Chen |
E116309
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Steven Shih Chen |
E116309
|
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: Steven Shih Chen | Statement: [Steven Shih Chen, name, Steven Shih Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steven Shih Chen Context triple: [Steven Shih Chen, name, Steven Shih Chen]
-
A.
Steven Shih Chen
chosen
Steven Shih Chen is a Taiwanese-American internet entrepreneur best known as a co-founder of YouTube.
-
B.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
C.
Kenneth Hsu
Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
-
D.
Yu-Chi Ho
Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
-
E.
Ben Chang
Ben Chang is a chaotic and eccentric Spanish teacher-turned-student from the TV sitcom "Community," known for his unpredictable behavior and over-the-top antics.
- 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_69bd445d94788190b72e2cc563120995 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79019c6481909641f173c5b3769a |
completed | March 20, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed0123bc48190920f60fc29f64734 |
completed | March 21, 2026, 5:06 p.m. |
Created at: March 20, 2026, 1:44 p.m.