Triple
T20391010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Philadelphia Experiment |
E498082
|
entity |
| Predicate | hasTimeTravelDestinationYear |
P22750
|
FINISHED |
| Object | 1984 |
—
|
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: 1984 | Statement: [The Philadelphia Experiment, hasTimeTravelDestinationYear, 1984]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTimeTravelDestinationYear Context triple: [The Philadelphia Experiment, hasTimeTravelDestinationYear, 1984]
-
A.
travelsToYear
chosen
Indicates that an entity moves or is transported from its original time to a specified calendar year.
-
B.
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.
-
C.
timeTravelTo
Indicates traveling from one point in time to another, typically different, point in time.
-
D.
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.
-
E.
timeTravelDirection
Indicates the temporal direction in which time travel occurs, such as moving into the past or into the future.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790f8d9c819093038f6bb6f47a92 |
completed | April 20, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69e57648be3c81908256838228cabf5c |
completed | April 20, 2026, 12:41 a.m. |
Created at: April 16, 2026, 11:28 a.m.