Triple
T37370735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Narva |
E927834
|
entity |
| Predicate | hasOffensiveForce |
P41989
|
FINISHED |
| Object | Russian besieging army |
—
|
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: Russian besieging army | Statement: [Battle of Narva, hasOffensiveForce, Russian besieging army]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOffensiveForce Context triple: [Battle of Narva, hasOffensiveForce, Russian besieging army]
-
A.
offensiveForce
chosen
Indicates the use or application of aggressive or attacking power or violence by one entity against another.
-
B.
hasOpposingForceType
Indicates that one force is characterized as being of a type that opposes or counteracts another force.
-
C.
offensiveStrength
Indicates the degree or capacity of an entity to carry out effective attacks or aggressive actions against an opponent.
-
D.
usedOffensiveSystem
Indicates that an entity employed an offensive system (such as a weapon or attack mechanism) against another entity or target.
-
E.
usesForces
Indicates that one entity applies physical, magical, or other types of forces to influence, move, or affect another entity.
- 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_69f76eb820248190a5c395ca50ad002a |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd0b92f42881908cd77e3f058adcc2 |
completed | May 7, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fd0a3d68d4819094d92040f7c48d7c |
completed | May 7, 2026, 9:55 p.m. |
Created at: May 3, 2026, 4:16 p.m.