Triple
T27134918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ESCA |
E681655
|
entity |
| Predicate | basedOnPhysicalEffect |
P41926
|
FINISHED |
| Object | photoelectric effect |
—
|
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: photoelectric effect | Statement: [ESCA, basedOnPhysicalEffect, photoelectric effect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnPhysicalEffect Context triple: [ESCA, basedOnPhysicalEffect, photoelectric effect]
-
A.
involvedPhysicalEffect
Indicates that one entity participates in causing, experiencing, or mediating a physical effect on another entity or the environment.
-
B.
hasPhysicalNature
Indicates that one entity possesses or exhibits a specific physical form, composition, or material nature in relation to another.
-
C.
physicalBasis
chosen
Indicates that one entity serves as the underlying physical foundation, mechanism, or substrate that gives rise to or supports the properties, behavior, or existence of another entity.
-
D.
capturesEffectOf
Indicates that one entity represents or records the impact, consequence, or outcome produced by another entity or process.
-
E.
affectsPhenomenon
Indicates that one phenomenon produces an influence or change on another phenomenon.
- 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_69eefacbcc2081909ebf00daa23f1981 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f7465687bc8190a9da44d62b634ed7 |
completed | May 3, 2026, 12:57 p.m. |
| PD | Predicate disambiguation | batch_69f743f4ceb08190a21fe7f4a99b166b |
completed | May 3, 2026, 12:47 p.m. |
Created at: April 27, 2026, 9:06 a.m.