Triple
T11355705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Chulalongkorn |
E268942
|
entity |
| Predicate | portraitOn |
P99419
|
FINISHED |
| Object | Thai banknotes |
—
|
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: Thai banknotes | Statement: [King Chulalongkorn, portraitOn, Thai banknotes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraitOn Context triple: [King Chulalongkorn, portraitOn, Thai banknotes]
-
A.
portraitSpecialization
Indicates that one entity specializes in creating or working with portraits, distinguishing a focused area of expertise within a broader artistic or professional domain.
-
B.
portrayerName
Indicates the name of the person who portrays or plays a particular character or role.
-
C.
portraitArtist
Indicates that one entity is the artist who created a portrait depicting the other entity.
-
D.
publicFigure
Indicates that an entity is widely recognized by the public and holds a prominent or influential role in society, such as in politics, entertainment, or media.
-
E.
publicImage
Indicates how an entity is perceived or represented by the general public or broader audience.
- 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_69d6aacbe18081909e5fadb50082dd96 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d80148e2048190a716b515d78efdd1 |
completed | April 9, 2026, 7:43 p.m. |
| PD | Predicate disambiguation | batch_69d7e6f8aeb4819080476f16a69b2ee3 |
completed | April 9, 2026, 5:50 p.m. |
| PDg | Predicate description generation | batch_69d801451b1c8190944b17906b354142 |
completed | April 9, 2026, 7:43 p.m. |
Created at: April 8, 2026, 9:33 p.m.