Triple
T20474748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edgar Wesley |
E502282
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Wesley |
—
|
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: Wesley | Statement: [Edgar Wesley, familyName, Wesley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wesley Context triple: [Edgar Wesley, familyName, Wesley]
-
A.
Wesley
chosen
Wesley is a masculine given name of English origin, traditionally used in English-speaking countries.
-
B.
Wesley
Wesley is a central character in Sam Shepard's play "Curse of the Starving Class," representing the disillusioned, volatile son in a dysfunctional American family.
-
C.
Wesley
Wesley is a character from the animated web series "The Cookout," known for his humorous role in the show's ensemble cast.
-
D.
Wesley Jonathan
Wesley Jonathan is an American actor best known for his roles in early-2000s television sitcoms and films, including the roller-skating comedy-drama "Roll Bounce."
-
E.
Elisha Christian
Elisha Christian is a cinematographer known for his work on feature films such as the romantic sci-fi drama "In Your Eyes."
- 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_69e0b4ae5f1081908768b0c9a3a0bf38 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6996464448190bdf9cf63c736d6dd |
completed | April 20, 2026, 9:23 p.m. |
Created at: April 16, 2026, 11:33 a.m.