Triple
T8668400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FuG 220 Lichtenstein SN-2 radar |
E205732
|
entity |
| Predicate | minimumRangeApproximate |
P26954
|
FINISHED |
| Object | 500 m |
—
|
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: 500 m | Statement: [FuG 220 Lichtenstein SN-2 radar, minimumRangeApproximate, 500 m]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimumRangeApproximate Context triple: [FuG 220 Lichtenstein SN-2 radar, minimumRangeApproximate, 500 m]
-
A.
rangeOf
Indicates that one entity specifies the set of possible values (range) that another entity’s outputs or properties can take.
-
B.
minimumNumber
chosen
Indicates that the associated value is the smallest or least quantity allowed, required, or observed within a given set or context.
-
C.
approximateRadius
Indicates that one entity specifies or provides an estimated value for the radius of another entity.
-
D.
boundedByApprox
Indicates that one quantity is constrained by another within an approximate or tolerance-based bound, rather than an exact strict limit.
-
E.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48a48b548190b78259072b1224ee |
completed | March 31, 2026, 10:20 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:31 p.m.