Triple
T16595801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Wong |
E403202
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mr. Wong |
E403202
|
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: Mr. Wong | Statement: [Mr. Wong, name, Mr. Wong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. Wong Context triple: [Mr. Wong, name, Mr. Wong]
-
A.
Mr. Wong
chosen
Mr. Wong is the enigmatic criminal mastermind and primary antagonist in the 1934 mystery film "The Mysterious Mr. Wong."
-
B.
Herb Wong
Herb Wong is a jazz educator, producer, and longtime radio broadcaster known for his influential role in promoting and documenting West Coast jazz.
-
C.
Jimmy Woo
Jimmy Woo is a Marvel Comics and Marvel Cinematic Universe character, depicted as an earnest and by-the-book FBI agent who often becomes entangled in superhero-related investigations.
-
D.
Señor Chang
Señor Chang is the unhinged, often antagonistic Spanish teacher-turned-student from the TV series "Community," known for his erratic behavior and over-the-top antics.
-
E.
Thai Lee
Thai Lee is a Korean-American entrepreneur and business executive best known as the co-founder, president, and CEO of SHI International, one of the largest woman-owned technology solutions providers in the United States.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e35d717604819083ed60b865bbd13e |
completed | April 18, 2026, 10:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00759fe6ec81908c5321dcba558269 |
completed | May 10, 2026, 12:10 p.m. |
Created at: April 10, 2026, 5:16 a.m.