Triple
T15308194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of Louis XIV in Coronation Robes |
E365957
|
entity |
| Predicate | depictsFootwear |
P118052
|
FINISHED |
| Object | red high-heeled shoes |
—
|
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: red high-heeled shoes | Statement: [Portrait of Louis XIV in Coronation Robes, depictsFootwear, red high-heeled shoes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsFootwear Context triple: [Portrait of Louis XIV in Coronation Robes, depictsFootwear, red high-heeled shoes]
-
A.
colorOfFoot
Indicates the specific color attribute associated with a foot.
-
B.
footType
Indicates the specific kind or classification of feet that an entity possesses or is characterized by.
-
C.
traditionalFootwear
Indicates that the relationship involves footwear that is characteristic of, or historically associated with, a particular culture, region, or tradition.
-
D.
footwearCategory
Indicates the classification of an item based on the type or category of footwear it belongs to.
-
E.
shoeLine
Indicates a relationship where a shoe is part of, or belongs to, a particular product line or collection of shoes.
- F. None of above. chosen
Provenance (4 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03cd001b48190bbdd69337efdb907 |
completed | April 16, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69deca935e2c8190b640987ddfc542b9 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2e413481909d9180a8d78d2c17 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:16 a.m.