Triple
T34647631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Open Water 2: Adrift |
E889741
|
entity |
| Predicate | usesRealisticSetting |
P184423
|
FINISHED |
| Object | true |
—
|
LITERAL 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: true | Statement: [Open Water 2: Adrift, usesRealisticSetting, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRealisticSetting Context triple: [Open Water 2: Adrift, usesRealisticSetting, true]
-
A.
portrayedInSetting
Indicates that an entity is depicted or represented within a particular setting, environment, or context.
-
B.
basedOnRealLocation
Indicates that something is derived from, inspired by, or modeled after an actual geographic place in the real world.
-
C.
usedAsSettingFor
Indicates that one entity serves as the backdrop, location, or environment in which another entity (such as an event, story, or activity) takes place.
-
D.
setInFictionalOrRealLocation
Indicates that something (such as a story, event, or scene) takes place within a specified location, whether that location is real or fictional.
-
E.
coversSetting
Indicates that one entity includes or addresses a particular setting or context within its scope.
- F. None of above. chosen
Provenance (4 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_69f349d825c88190bfc6170ac9281260 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69f7b0e3917481908a394680d76743c3 |
completed | May 3, 2026, 8:32 p.m. |
Created at: May 1, 2026, 2:04 a.m.