Triple
T21417390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Fitzmaurice |
E528338
|
entity |
| Predicate | directed |
P7373
|
FINISHED |
| Object | Suzy |
E1165454
|
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: Suzy | Statement: [George Fitzmaurice, directed, Suzy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzy Context triple: [George Fitzmaurice, directed, Suzy]
-
A.
Suzy
Suzy is the central female protagonist of John Steinbeck’s novel "Sweet Thursday," known for her independent spirit and evolving relationship with Doc in the Cannery Row community.
-
B.
Suzy
chosen
Suzy is a fictional character from the film "Cashback," portrayed by actress Michelle Ryan.
-
C.
Suzy
Suzy is an American YouTuber, artist, and animator best known for her work on the channel Mortem3r and her appearances in the Game Grumps community.
-
D.
Suzie
Suzie is a brilliant, tech-savvy girl from Stranger Things who helps Dustin Henderson and his friends by providing crucial scientific and hacking assistance.
-
E.
Suzie
Suzie is a Canadian film written and directed by Micheline Lanctôt, known for its intimate, character-driven storytelling.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b2073a7881909adda8ed70a2cecd |
completed | April 22, 2026, 11:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09c2a06c0c81909da8bb39a78df85e |
completed | May 17, 2026, 1:29 p.m. |
Created at: April 16, 2026, 5:46 p.m.