Triple
T7253272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IceCube Neutrino Observatory |
E157655
|
entity |
| Predicate | detectorVolume |
P1567
|
FINISHED |
| Object | 1 cubic kilometer |
—
|
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: 1 cubic kilometer | Statement: [IceCube Neutrino Observatory, detectorVolume, 1 cubic kilometer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: detectorVolume Context triple: [IceCube Neutrino Observatory, detectorVolume, 1 cubic kilometer]
-
A.
detectorType
Indicates the specific kind or category of detector associated with an entity or measurement.
-
B.
foundInVolume
Indicates that one entity is physically or logically contained within, or occurs in, a specific volume (such as a book volume, data volume, or bounded collection).
-
C.
numberOfDetectors
Indicates the quantity of detectors associated with or involved in a given entity or system.
-
D.
volume
chosen
Indicates the amount of three-dimensional space an entity occupies or contains.
-
E.
vertexDetectorTechnology
Indicates the specific detection technology or method used in the vertex detector component of a particle physics or tracking system.
- 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_69c6882d81d4819085f7ff862951ee4f |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea9d41908190bb76c6a5b9d5b1a2 |
completed | March 27, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69c6e7666ffc81908bf643d8257e6337 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:56 p.m.