Triple
T33591921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaz Granger |
E860447
|
entity |
| Predicate | hasTimeTravelContext |
P51702
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Shaz Granger, hasTimeTravelContext, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTimeTravelContext Context triple: [Shaz Granger, hasTimeTravelContext, yes]
-
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.
hasTimeDepth
Indicates that something possesses or spans a measurable extent of time, such as duration, historical depth, or temporal layering.
-
C.
timeTravelElement
chosen
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.
-
D.
hasDateContext
Indicates that something is associated with, constrained by, or interpreted within a specific date or time-related context.
-
E.
hasFictionalTimeAfter
Indicates that one fictional time point or period occurs later than another within a narrative or imagined timeline.
- 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_69f3497e70e48190951c94d072879bec |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd5d48855c8190bd93070b6a00d8b5 |
completed | May 8, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69fd5c9aabb88190912800d90184a89d |
completed | May 8, 2026, 3:46 a.m. |
Created at: May 1, 2026, 1:40 a.m.