Triple
T34391917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lewis Machine & Tool Company |
E882725
|
entity |
| Predicate | typeOfWeapons |
P16410
|
FINISHED |
| Object | semi-automatic rifles |
—
|
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: semi-automatic rifles | Statement: [Lewis Machine & Tool Company, typeOfWeapons, semi-automatic rifles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfWeapons Context triple: [Lewis Machine & Tool Company, typeOfWeapons, semi-automatic rifles]
-
A.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
-
B.
weaponCategory
chosen
Indicates the classification or type of weapon to which an item or armament belongs.
-
C.
weaponsUsed
Indicates that one entity employed or utilized another entity as a weapon in carrying out an action or event.
-
D.
weaponsRepresent
Indicates that certain weapons serve as symbols or embodiments of something, such as ideas, groups, or values.
-
E.
involvesWeaponType
Indicates that the relationship or action includes the use, presence, or association of a specific type or category of weapon.
- 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_69f349c1304081909331872829e38106 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a01302a80ec8190a27692f09ac38a80 |
completed | May 11, 2026, 1:26 a.m. |
| PD | Predicate disambiguation | batch_6a012fad62308190a53f8b4a071eb245 |
completed | May 11, 2026, 1:23 a.m. |
Created at: May 1, 2026, 1:59 a.m.