Triple
T34253619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French 16th Division |
E878811
|
entity |
| Predicate | usedRifles |
P87035
|
FINISHED |
| Object | Lebel rifle |
—
|
NE NERFINISHED |
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: Lebel rifle | Statement: [French 16th Division, usedRifles, Lebel rifle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedRifles Context triple: [French 16th Division, usedRifles, Lebel rifle]
-
A.
riflesRepresent
Indicates that rifles serve as symbols, embodiments, or representations of something (such as ideas, groups, or values) in a given context.
-
B.
isServiceRifleOf
chosen
Indicates that one entity is the designated service rifle used by another entity, typically a military or armed force.
-
C.
weaponsUsed
Indicates that one entity employed or utilized another entity as a weapon in carrying out an action or event.
-
D.
lightArmament
Indicates that an entity is equipped with or characterized by relatively minimal or lightweight weaponry compared to standard or heavy armament.
-
E.
numberOfGuns
Indicates the quantity of guns associated with a given entity or situation.
- 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_69f349b421cc8190b4b4655e1d612548 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71362f1448190985a80ce7af475cb |
completed | May 3, 2026, 9:20 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
Created at: May 1, 2026, 1:56 a.m.