Triple
T15443453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | His Ducal Serene Highness |
E369966
|
entity |
| Predicate | correspondingFemaleStyle |
P118808
|
FINISHED |
| Object | Her Ducal Serene Highness |
—
|
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: Her Ducal Serene Highness | Statement: [His Ducal Serene Highness, correspondingFemaleStyle, Her Ducal Serene Highness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correspondingFemaleStyle Context triple: [His Ducal Serene Highness, correspondingFemaleStyle, Her Ducal Serene Highness]
-
A.
fashionStyle
Indicates the characteristic way in which an entity dresses or presents themselves in terms of clothing and appearance.
-
B.
womenSection
Indicates that something is designated as belonging to, located in, or associated with the women's section or area.
-
C.
personHasNotableStyle
Indicates that a person is recognized for having a distinctive or noteworthy style.
-
D.
femaleFeature
Indicates that the subject possesses a characteristic or attribute that is typically associated with females.
-
E.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ef55f5c8190a32b1b6ad1daf454 |
completed | April 16, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ded28276f481908c2038bb301e57cf |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded57005608190886cd01f640dfedb |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 3:21 a.m.