Triple
T22977909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara Gruen |
E571375
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Water for Elephants |
—
|
NE NERFINISHED |
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: Water for Elephants | Statement: [Sara Gruen, notableWork, Water for Elephants]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Water for Elephants Context triple: [Sara Gruen, notableWork, Water for Elephants]
-
A.
Water for Elephants
chosen
Water for Elephants is a romantic drama film set in a Depression-era traveling circus, adapted from Sara Gruen’s novel and known for its blend of romance, hardship, and spectacle.
-
B.
The Zookeeper
The Zookeeper is a film featuring Czech actor Karel Roden in a prominent role.
-
C.
We the Animals
We the Animals is a 2018 coming-of-age drama film, adapted from Justin Torres’s novel, that follows three young brothers growing up in a turbulent, mixed-race working-class family.
-
D.
Zookeeper’s Wife
Zookeeper’s Wife is a historical drama film based on the true story of a Warsaw zookeeper and his wife who helped save hundreds of Jews during World War II.
-
E.
The Fawn
The Fawn is a satirical Jacobean stage comedy by John Marston that lampoons courtly manners and political intrigue.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18292f3788190ab4e9d559e0070c8 |
completed | April 29, 2026, 4:01 a.m. |
Created at: April 17, 2026, 3:49 p.m.