Triple
T29297742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guap |
E742877
|
entity |
| Predicate | featuresCityInVideo |
P4219
|
FINISHED |
| Object | Detroit |
—
|
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: Detroit | Statement: [Guap, featuresCityInVideo, Detroit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCityInVideo Context triple: [Guap, featuresCityInVideo, Detroit]
-
A.
featuresModelInVideo
Indicates that a video includes or showcases a particular model as part of its visual content.
-
B.
featuresLocation
Indicates that something is characterized by or includes a specific location as one of its notable attributes.
-
C.
sceneFeature
Indicates a characteristic, element, or attribute that is present within or helps define a particular scene.
-
D.
videoFeatures
Indicates that one entity possesses or includes specific characteristics, attributes, or elements of a video.
-
E.
cityPanorama
chosen
Indicates a wide, comprehensive visual view or representation of a cityscape, typically encompassing many of its features in a single scene.
- 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_69f0912323c48190b9a24ef8cf359225 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69ff4de66ba481908e7184b3cf9d4d2d |
completed | May 9, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69ff4c702a5881909c6684c74807e945 |
completed | May 9, 2026, 3:02 p.m. |
Created at: April 28, 2026, 1:07 p.m.