Triple
T35469379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sean Maher as Simon Tam |
E1025165
|
entity |
| Predicate | shipInStory |
P35676
|
FINISHED |
| Object | Serenity (Firefly-class transport ship) |
—
|
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: Serenity (Firefly-class transport ship) | Statement: [Sean Maher as Simon Tam, shipInStory, Serenity (Firefly-class transport ship)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shipInStory Context triple: [Sean Maher as Simon Tam, shipInStory, Serenity (Firefly-class transport ship)]
-
A.
shipCompanion
Indicates that one entity serves as a companion or partner accompanying another entity on a ship or sea voyage.
-
B.
appearsInShortStoryBy
Indicates that one entity is a character, element, or subject that appears within a short story authored by another entity.
-
C.
stakesInStory
Indicates that one entity has a personal investment, risk, or potential gain/loss tied to the outcome of another entity’s story or narrative.
-
D.
fieldInStory
Indicates that a particular field or attribute appears within, or is defined as part of, a given story.
-
E.
storyElement
chosen
Indicates that one entity functions as a narrative component or part within the structure of another entity’s story.
- F. None of above.
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_69f76dfa20d0819089585dc2cf653aea |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:04 p.m.