Triple
T6126809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Downsizing |
E136614
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Kristen Wiig |
E67174
|
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: Kristen Wiig | Statement: [Downsizing, starring, Kristen Wiig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristen Wiig Context triple: [Downsizing, starring, Kristen Wiig]
-
A.
Kristen Wiig
chosen
Kristen Wiig is an American comedian, actress, and writer best known for her work on Saturday Night Live and films such as Bridesmaids.
-
B.
Melissa McCarthy
Melissa McCarthy is an American actress and comedian known for her breakout comedic role in "Bridesmaids" and subsequent work in film and television.
-
C.
Maya Rudolph
Maya Rudolph is an American actress and comedian known for her work on "Saturday Night Live" and in numerous film and animated voice roles.
-
D.
Amy Poehler
Amy Poehler is an American comedian, actress, writer, and producer best known for her work on "Saturday Night Live" and for starring as Leslie Knope on the sitcom "Parks and Recreation."
-
E.
Tina Fey
Tina Fey is an American comedian, writer, actress, and producer best known for her work on "Saturday Night Live" and creating the acclaimed sitcom "30 Rock."
- 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_69c008a0a37c81908e5b4f879158afb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c2a13a48190b80e11d58fc87c8a |
completed | March 22, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1416a4dd481908f1336fbd02c7af8 |
completed | March 23, 2026, 1:34 p.m. |
Created at: March 22, 2026, 4:15 p.m.