Triple
T16661900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait of Charles I in Three Positions |
E404878
|
entity |
| Predicate | depictsHair |
P16252
|
FINISHED |
| Object | long dark hair of Charles I |
—
|
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: long dark hair of Charles I | Statement: [Portrait of Charles I in Three Positions, depictsHair, long dark hair of Charles I]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsHair Context triple: [Portrait of Charles I in Three Positions, depictsHair, long dark hair of Charles I]
-
A.
depictsHairColor
Indicates that one entity visually represents or portrays the hair color of another entity.
-
B.
hasHair
Indicates that an entity possesses hair as a physical attribute.
-
C.
hairAsSymbol
Indicates that hair functions as a symbolic element representing ideas, traits, or meanings beyond its literal physical presence.
-
D.
hairDetail
chosen
Indicates a relationship that specifies particular characteristics or attributes of an entity’s hair, such as style, color, length, or texture.
-
E.
hairType
Indicates the specific kind or category of hair an entity has, such as its texture, style, or structural type.
- 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_69d8838b5fbc81908c6575c132b82e80 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37bfee5448190bb44c4dadba5dcbd |
completed | April 18, 2026, 12:41 p.m. |
| PD | Predicate disambiguation | batch_69e319b1d7f08190b5ecb4a68c636c15 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:18 a.m.