Triple
T16754244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M67 fragmentation grenade |
E407167
|
entity |
| Predicate | maximumFragmentHazardRange |
P23477
|
FINISHED |
| Object | up to 230 meters |
—
|
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: up to 230 meters | Statement: [M67 fragmentation grenade, maximumFragmentHazardRange, up to 230 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumFragmentHazardRange Context triple: [M67 fragmentation grenade, maximumFragmentHazardRange, up to 230 meters]
-
A.
hazardScope
chosen
Indicates the range or extent within which a particular hazard is relevant, applicable, or has effect.
-
B.
fragmentationLevel
Indicates the degree to which something is broken into smaller, separate parts or segments.
-
C.
hasHazardLevel
Indicates that an entity is associated with a specified degree or category of risk or danger.
-
D.
numberOfFragmentsApprox
Indicates an approximate count of how many fragments or pieces are associated with the subject.
-
E.
maximumNumberOfSegments
Indicates the greatest allowable or observed count of discrete segments into which something can be or is divided.
- 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3abe6b68c8190a5e2a11973f01b8e |
completed | April 18, 2026, 4:05 p.m. |
| PD | Predicate disambiguation | batch_69e319cbd79c8190a03587a61c18bec0 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:21 a.m.