Triple
T17713134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EAR1 |
E441619
|
entity |
| Predicate | hasDetectorTypes |
P7243
|
FINISHED |
| Object | calorimetric detectors |
—
|
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: calorimetric detectors | Statement: [EAR1, hasDetectorTypes, calorimetric detectors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDetectorTypes Context triple: [EAR1, hasDetectorTypes, calorimetric detectors]
-
A.
detectorType
chosen
Indicates the specific kind or category of detector associated with an entity or measurement.
-
B.
hasDiscoveryType
Indicates the specific manner, method, or category by which something was discovered.
-
C.
hasNumberOfTypes
Indicates that an entity is associated with a specific count of distinct types or categories it possesses or includes.
-
D.
hasFarDetector
Indicates that an entity is equipped with or associated with a detector positioned at a relatively large distance from a reference point or source.
-
E.
numberOfDetectors
Indicates the quantity of detectors associated with or involved in a given entity or 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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4729cebd08190872be96a26d0f7ce |
completed | April 19, 2026, 6:13 a.m. |
| PD | Predicate disambiguation | batch_69e3cde601d4819097903f471f1fe99a |
completed | April 18, 2026, 6:31 p.m. |
Created at: April 10, 2026, 10:06 a.m.