Triple
T8631783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FuG 212 Lichtenstein C-1 radar |
E204419
|
entity |
| Predicate | typicalDetectionRange |
P13474
|
FINISHED |
| Object | several kilometers against bomber-sized targets |
—
|
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: several kilometers against bomber-sized targets | Statement: [FuG 212 Lichtenstein C-1 radar, typicalDetectionRange, several kilometers against bomber-sized targets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDetectionRange Context triple: [FuG 212 Lichtenstein C-1 radar, typicalDetectionRange, several kilometers against bomber-sized targets]
-
A.
typicalRange
chosen
Indicates the usual or expected range of values, conditions, or states within which something normally occurs or applies.
-
B.
typicalEnergyRange
Indicates the usual or characteristic range of energy values associated with an entity, process, or interaction.
-
C.
typicalTrackLengthRange
Indicates the usual minimum and maximum lengths that a track associated with something tends to fall between.
-
D.
detectorType
Indicates the specific kind or category of detector associated with an entity or measurement.
-
E.
radarType
Indicates the specific category or classification of radar associated with an entity.
- 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_69ca834b903c8190add96cc651e1a477 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5730309081909a9a0256c9bf5f8f |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc455906f8819082edd79cb4a1cf28 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:27 p.m.