Triple
T9565221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Futurama |
E230772
|
entity |
| Predicate | protagonistTimeDisplacement |
P10440
|
FINISHED |
| Object | 1000 years into the future |
—
|
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: 1000 years into the future | Statement: [Futurama, protagonistTimeDisplacement, 1000 years into the future]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistTimeDisplacement Context triple: [Futurama, protagonistTimeDisplacement, 1000 years into the future]
-
A.
usesTimeTravelFor
Indicates a relationship where an entity employs time travel as a means or method to achieve, affect, or interact with another entity or objective.
-
B.
timeJump
Indicates a discontinuous transition of an entity from one point in time to another, skipping the intervening duration.
-
C.
timeTravelTo
chosen
Indicates traveling from one point in time to another, typically different, point in time.
-
D.
timeTravelDirection
Indicates the temporal direction in which time travel occurs, such as moving into the past or into the future.
-
E.
timeTravelElement
Indicates that the situation, event, or narrative involves an element of time travel, such as moving between different points in time or altering temporal sequences.
- 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_69ca847e53a88190a60eed7e02257f10 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd996a01b081908e2782f41520f73d |
completed | April 1, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69ccd594d0ac8190a81bc11a3a538167 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:04 p.m.