Triple
T17784559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ocean Without a Shore |
E443982
|
entity |
| Predicate | temporalFeature |
P29111
|
FINISHED |
| Object | looped video sequence |
—
|
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: looped video sequence | Statement: [Ocean Without a Shore, temporalFeature, looped video sequence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalFeature Context triple: [Ocean Without a Shore, temporalFeature, looped video sequence]
-
A.
tempoFeature
Indicates a relationship where a musical or rhythmic element is characterized by, or associated with, a specific tempo-related property or feature.
-
B.
temporal
Indicates a relationship that situates one event, state, or entity in time relative to another (e.g., before, after, or during).
-
C.
temporalAspect
chosen
Indicates the time-related characteristics or phase (such as duration, frequency, or temporal status) associated with an event or relationship.
-
D.
hasTemporalResolution
Indicates that one entity specifies the level of temporal detail or granularity at which another entity’s data, observation, or process is measured or represented.
-
E.
temporalAwareness
Indicates an entity’s ability to perceive, understand, or track the passage and ordering of time-related events.
- 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48791dec0819090d6e88449389fc0 |
completed | April 19, 2026, 7:43 a.m. |
| PD | Predicate disambiguation | batch_69e3d8d8e538819084f1584426b41d5e |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:12 a.m.