Triple
T2659676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Portrait Series banknotes |
E54695
|
entity |
| Predicate | hasPortraitOrientation |
P12663
|
FINISHED |
| Object | obverse side |
—
|
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: obverse side | Statement: [Portrait Series banknotes, hasPortraitOrientation, obverse side]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPortraitOrientation Context triple: [Portrait Series banknotes, hasPortraitOrientation, obverse side]
-
A.
hasPortrait
Indicates that one entity possesses, displays, or is associated with a portrait depicting another entity.
-
B.
hasOrientation
chosen
Indicates that one entity is positioned or directed in a specific spatial or conceptual alignment relative to a reference frame or another entity.
-
C.
hasLandscapeType
Indicates that an entity possesses or is characterized by a particular type or category of landscape.
-
D.
sessionOrientation
Indicates the directional or spatial alignment relationship established between entities within a session or interaction context.
-
E.
hasFieldOrientation
Indicates that one entity has a specified directional or spatial orientation relative to a field (such as magnetic, electric, or visual field).
- 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_69ab49e028948190b97e01d73548b1d9 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd94f3b1881909bd36cfe61c254a5 |
completed | March 7, 2026, 7:52 a.m. |
| PD | Predicate disambiguation | batch_69abd81768748190bd965f367cf6ef37 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:53 p.m.