Triple
T16811093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teri Lee |
E408611
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Margaret Cho |
E162297
|
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: Margaret Cho | Statement: [Teri Lee, portrayedBy, Margaret Cho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margaret Cho Context triple: [Teri Lee, portrayedBy, Margaret Cho]
-
A.
Margaret Cho
chosen
Margaret Cho is an American stand-up comedian, actress, and social activist known for her outspoken commentary on race, sexuality, and politics.
-
B.
Kathy Griffin
Kathy Griffin is an American stand-up comedian and actress known for her sharp, often controversial humor and frequent television appearances.
-
C.
Sally Diller
Sally Diller is a daughter of pioneering American stand-up comedian and actress Phyllis Diller.
-
D.
Jodi Lyn O'Keefe
Jodi Lyn O'Keefe is an American actress and model best known for her roles in films like "She's All That" and TV series such as "Nash Bridges" and "Prison Break."
-
E.
Wanda Sykes
Wanda Sykes is an American stand-up comedian, actress, and writer known for her sharp, outspoken humor and roles in television, film, and voice acting.
- 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_69e3b2d0793c81909d938ac174a6e63a |
completed | April 18, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b290d8e4819082880444b42ffa43 |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.