Triple
T16799438
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Wexler |
E408316
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Kimberly Wexler |
E408316
|
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: Kimberly Wexler | Statement: [Kim Wexler, alsoKnownAs, Kimberly Wexler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kimberly Wexler Context triple: [Kim Wexler, alsoKnownAs, Kimberly Wexler]
-
A.
Kim Wexler
chosen
Kim Wexler is a highly skilled and morally conflicted attorney whose complex relationship with Jimmy McGill/Saul Goodman is central to the character-driven drama of Better Call Saul.
-
B.
Risa Gertner
Risa Gertner is a film producer known for her work on the romantic comedy "The First Time."
-
C.
Jenny Schecter
Jenny Schecter is a central, often controversial character in the television drama "The L Word," known for her complex personal evolution, troubled relationships, and career as a writer.
-
D.
Michael Ross
Michael Ross was an American television writer and producer best known for co-creating influential sitcoms such as The Jeffersons.
-
E.
Michael Ross
Michael Ross is a film editor known for his work on the documentary "The True Cost," which examines the global impact of the fashion industry.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2abc430819080c1303eded5f416 |
completed | April 18, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00ab1299ac81908e9f1eebc3424bb9 |
completed | May 10, 2026, 3:58 p.m. |
Created at: April 10, 2026, 5:22 a.m.