Triple
T10221923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christina of Denmark portrait |
E242600
|
entity |
| Predicate | sitterAgeAtTimeOfPortrait |
P92820
|
FINISHED |
| Object | about 16 |
—
|
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: about 16 | Statement: [Christina of Denmark portrait, sitterAgeAtTimeOfPortrait, about 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sitterAgeAtTimeOfPortrait Context triple: [Christina of Denmark portrait, sitterAgeAtTimeOfPortrait, about 16]
-
A.
sitterNationality
Indicates the national identity or citizenship of the person who is sitting for a portrait or being depicted.
-
B.
sitterBirthPlace
Indicates the location where the person serving as the sitter was born.
-
C.
sitter
Indicates that one entity is serving as a caretaker or guardian, typically watching over or looking after another entity.
-
D.
portraysAgeGroup
Indicates that one entity depicts or represents another entity as belonging to a particular age group.
-
E.
typicalAge
Indicates the usual or characteristic age associated with an entity, event, or condition.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa740db88190ba707226e36de0ed |
completed | April 6, 2026, 12:43 p.m. |
| PD | Predicate disambiguation | batch_69d3955f61f88190b8d37ff645cd44d3 |
completed | April 6, 2026, 11:13 a.m. |
| PDg | Predicate description generation | batch_69d3aa208c248190a0fb186b106389f3 |
completed | April 6, 2026, 12:42 p.m. |
Created at: April 6, 2026, 11:10 a.m.