Triple
T32036829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lock n' Load with R. Lee Ermey |
E818116
|
entity |
| Predicate | featuresWeaponType |
P16410
|
FINISHED |
| Object | small arms |
—
|
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: small arms | Statement: [Lock n' Load with R. Lee Ermey, featuresWeaponType, small arms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresWeaponType Context triple: [Lock n' Load with R. Lee Ermey, featuresWeaponType, small arms]
-
A.
weaponCategory
chosen
Indicates the classification or type of weapon to which an item or armament belongs.
-
B.
featuredWeapon
Indicates that a particular weapon is highlighted or prominently showcased in relation to an entity, such as a character, event, or context.
-
C.
weaponFamily
Indicates that two weapons belong to the same broader classification or type group based on shared characteristics or lineage.
-
D.
weaponExamples
Indicates that one entity is an example or instance of a weapon associated with another entity.
-
E.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
- 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_69f348fbc8148190b3c0f95d4772b153 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a00acf86e4081909aad9356650e0f79 |
completed | May 10, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_6a00ac813fd08190a0a7782609e74e70 |
completed | May 10, 2026, 4:04 p.m. |
Created at: May 1, 2026, 12:18 a.m.