Triple
T444349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Officer and Laughing Girl |
E10183
|
entity |
| Predicate | usesLightSource |
P1280
|
FINISHED |
| Object | window at left |
—
|
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: window at left | Statement: [Officer and Laughing Girl, usesLightSource, window at left]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesLightSource Context triple: [Officer and Laughing Girl, usesLightSource, window at left]
-
A.
lightSourceFor
Indicates that one entity serves as the source of illumination for another entity.
-
B.
hasLighting
chosen
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
C.
lightRange
Indicates the distance or area over which a light source effectively emits or illuminates.
-
D.
lightLevel
Indicates the intensity or amount of light present in a given context or environment.
-
E.
lightPath
Indicates the route or trajectory that light follows as it travels between entities or through a medium.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef459800819083cd9eef3e7b5295 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2edde2b9c8190bd20b582eb4c5065 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.