Triple
T16523299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hospitality of Abraham mosaic |
E401372
|
entity |
| Predicate | visualFeatures |
P25983
|
FINISHED |
| Object | seated angels around a table |
—
|
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: seated angels around a table | Statement: [Hospitality of Abraham mosaic, visualFeatures, seated angels around a table]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualFeatures Context triple: [Hospitality of Abraham mosaic, visualFeatures, seated angels around a table]
-
A.
visualFeature
chosen
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
B.
aiFeatures
Indicates that an entity possesses, supports, or is associated with specific artificial intelligence capabilities or functionalities.
-
C.
featuresModelInVideo
Indicates that a video includes or showcases a particular model as part of its visual content.
-
D.
sharesFeatureExtractor
Indicates that two or more models or components use the same feature extraction mechanism or module.
-
E.
sceneFeature
Indicates a characteristic, element, or attribute that is present within or helps define a particular 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_69d883838abc8190bc79cb2d41733ce2 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32ed2622c8190b6429a49ca92284a |
completed | April 18, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69e296995d388190b88ebe189dce890d |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:14 a.m.