Triple
T5532866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enter Nowhere |
E145089
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Katherine Waterston |
E524831
|
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: Katherine Waterston | Statement: [Enter Nowhere, hasCastMember, Katherine Waterston]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katherine Waterston Context triple: [Enter Nowhere, hasCastMember, Katherine Waterston]
-
A.
Katherine Waterston
chosen
Katherine Waterston is an American actress known for roles in films such as "Inherent Vice," the "Fantastic Beasts" series, and "Alien: Covenant."
-
B.
Maggie Siff
Maggie Siff is an American actress best known for her television roles in series such as Mad Men, Sons of Anarchy, and Billions.
-
C.
Madeleine Stowe
Madeleine Stowe is an American actress best known for her film roles in the 1990s, including "The Last of the Mohicans" and "12 Monkeys," and later for her acclaimed television work.
-
D.
Jemima Kirke
Jemima Kirke is a British-American artist and actress best known for playing Jessa Johansson on the HBO series "Girls."
-
E.
Rebecca Ferguson
Rebecca Ferguson is a Swedish actress known for her versatile performances in films such as the Mission: Impossible series, The Greatest Showman, and Dune.
- 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_69c008f9955881909bfa8348b56b4739 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f9ea2c88190a68642f5799bd8ff |
completed | March 22, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04ce1bce4819095559af19cf072f8 |
completed | March 22, 2026, 8:11 p.m. |
Created at: March 22, 2026, 3:34 p.m.