Triple
T3422354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mutiny on the Bounty (1935 film) |
E72141
|
entity |
| Predicate | hasSeafaringTheme |
P48624
|
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: [Mutiny on the Bounty (1935 film), hasSeafaringTheme, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeafaringTheme Context triple: [Mutiny on the Bounty (1935 film), hasSeafaringTheme, true]
-
A.
hasMaritimeHeritage
Indicates that an entity possesses a historical, cultural, or traditional connection to maritime activities, seafaring, or the sea.
-
B.
hasNavalComponent
Indicates that something includes, involves, or is associated with a naval or maritime element as part of its composition or structure.
-
C.
maritimeSettingFor
Indicates that one entity serves as the maritime or ocean-related environment or backdrop in which another entity is situated or occurs.
-
D.
usesAtSea
Indicates that something is employed, operated, or applied in a maritime or oceanic environment.
-
E.
hasNotableSea
Indicates that an entity is associated with or contains a sea that is considered notable or significant.
- 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_69ad85ad38e48190b7660c5118a35289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb95223e081908b2954769d2f46c8 |
completed | March 8, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69adadfea024819094b41a13bc004bda |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb00f4f8c81908f88daf71f6a9c29 |
completed | March 8, 2026, 5:21 p.m. |
Created at: March 8, 2026, 3:15 p.m.