Triple
T13594200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Type 30 bayonet |
E324771
|
entity |
| Predicate | markings |
P35665
|
FINISHED |
| Object | arsenal markings on ricasso |
—
|
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: arsenal markings on ricasso | Statement: [Type 30 bayonet, markings, arsenal markings on ricasso]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: markings Context triple: [Type 30 bayonet, markings, arsenal markings on ricasso]
-
A.
marksOn
chosen
Indicates that one entity bears visible signs, traces, or imprints that have been made or left by another entity.
-
B.
leafMarkings
Indicates the presence, pattern, or characteristics of markings found on the surface of a leaf.
-
C.
distinctiveMarking
Indicates that one entity bears a unique or distinguishing visual feature or pattern that sets it apart from others.
-
D.
eggMarkings
Indicates that one entity bears or displays specific markings or patterns on its eggs in relation to another entity or context.
-
E.
mayHaveMarkings
Indicates that an entity is permitted or able to possess certain markings or distinguishing signs.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb057f1c881909a3bb77c659a724a |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae18eaf48190809e8b365856cde9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.