Triple
T5910964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | harpe sword |
E131455
|
entity |
| Predicate | relatedToWeaponType |
P67648
|
FINISHED |
| Object | kopis |
—
|
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: kopis | Statement: [harpe sword, relatedToWeaponType, kopis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedToWeaponType Context triple: [harpe sword, relatedToWeaponType, kopis]
-
A.
associatedWithWeapon
Indicates that an entity has a connection or involvement with a weapon, such as ownership, use, presence, or relevance in a given context.
-
B.
involvesWeaponType
Indicates that the relationship or action includes the use, presence, or association of a specific type or category of weapon.
-
C.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
-
D.
hasWeaponType
Indicates that an entity is associated with or equipped with a specific type or category of weapon.
-
E.
weaponTypeTested
Indicates that a specific type of weapon has been subjected to a test or evaluation in the described context.
- F. None of above. chosen
Provenance (4 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_69c008593a44819081a07ae0efe6c574 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c048fc112c8190b905bf561c9de096 |
completed | March 22, 2026, 7:54 p.m. |
| PD | Predicate disambiguation | batch_69c03352208c8190968efed05a9fd416 |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c048fb02a081908e78629e1e283cd0 |
completed | March 22, 2026, 7:54 p.m. |
Created at: March 22, 2026, 3:59 p.m.