Triple
T6033883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BaBar detector |
E134369
|
entity |
| Predicate | calorimeterTechnology |
P68878
|
FINISHED |
| Object | CsI(Tl) crystal calorimeter |
—
|
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: CsI(Tl) crystal calorimeter | Statement: [BaBar detector, calorimeterTechnology, CsI(Tl) crystal calorimeter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: calorimeterTechnology Context triple: [BaBar detector, calorimeterTechnology, CsI(Tl) crystal calorimeter]
-
A.
scientificInstruments
Indicates that one entity is a scientific instrument used by, associated with, or relevant to another entity in the context of scientific measurement or research.
-
B.
fuelProductionTechnology
Indicates the type of technology or method used to produce a particular fuel.
-
C.
emitsMoreEnergyThanItReceives
Indicates that the subject releases a greater amount of energy than it takes in from its surroundings or inputs.
-
D.
hasMeltingMechanism
Indicates that an entity possesses a specific mechanism or process by which it melts or causes melting.
-
E.
isCryogenic
Indicates that something operates at, is designed for, or involves extremely low (cryogenic) temperatures.
- 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_69c0087515148190a97475d412563865 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056b220608190b156be95632cf3b3 |
completed | March 22, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69c049e9a68c81909da0cfe4779ce9b5 |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8c5bfc8190b986a7071d1b23e3 |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:08 p.m.