Triple
T30380082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jake Gyllenhaal as Lou Bloom |
E772797
|
entity |
| Predicate | reportsToInFiction |
P131086
|
FINISHED |
| Object | Nina Romina |
—
|
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: Nina Romina | Statement: [Jake Gyllenhaal as Lou Bloom, reportsToInFiction, Nina Romina]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reportsToInFiction Context triple: [Jake Gyllenhaal as Lou Bloom, reportsToInFiction, Nina Romina]
-
A.
relatedToInFiction
Indicates that one entity is connected to another within a fictional context, such as a story, universe, or narrative work.
-
B.
targetInFiction
Indicates that one entity is the target or subject of an action, focus, or effect within a fictional work or narrative context.
-
C.
createsInFiction
Indicates that one entity is the creator or originator of another entity within a fictional or narrative context.
-
D.
conductsInFiction
Indicates that an entity carries out or performs an action or role within a fictional context or narrative.
-
E.
worksInFictionalContext
chosen
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
- 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_69f2248e3444819081b05712dc6873de |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b7b03488190b1db5fde4c7dd6e5 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8 p.m.