Triple
T1144851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poisson spot |
E23540
|
entity |
| Predicate | hasPhysicalNature |
P24432
|
FINISHED |
| Object | bright point of light |
—
|
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: bright point of light | Statement: [Poisson spot, hasPhysicalNature, bright point of light]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhysicalNature Context triple: [Poisson spot, hasPhysicalNature, bright point of light]
-
A.
hasNature
Indicates that something possesses, exhibits, or is characterized by a particular inherent quality, essence, or fundamental type.
-
B.
naturalOrArtificial
Indicates that something is classified as either naturally occurring or artificially created.
-
C.
hasPhysicalFootprint
Indicates that one entity occupies or affects a specific physical area or space in the real world.
-
D.
includesPhysicalWorld
Indicates that one entity encompasses, contains, or incorporates aspects or elements of the physical world within its scope or definition.
-
E.
hasNaturalFeature
Indicates that one entity possesses, contains, or is characterized by a particular natural feature (such as a mountain, river, forest, or coastline).
- 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc51244c8190bcd533f3e80c8f17 |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4d4104819084027a043c6118cb |
completed | March 1, 2026, 10:18 p.m. |
| PDg | Predicate description generation | batch_69a4bbb9fb4c81909dd39c496893c21b |
completed | March 1, 2026, 10:20 p.m. |
Created at: March 1, 2026, 7:44 p.m.