Triple
T7443450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HF/DF |
E171810
|
entity |
| Predicate | opposedForcesUsers |
P76425
|
FINISHED |
| Object | Axis radio operators as targets |
—
|
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: Axis radio operators as targets | Statement: [HF/DF, opposedForcesUsers, Axis radio operators as targets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: opposedForcesUsers Context triple: [HF/DF, opposedForcesUsers, Axis radio operators as targets]
-
A.
opposingForce
Indicates a relationship where one entity actively resists, counters, or works against the actions, goals, or influence of another entity.
-
B.
opposingForcesStatus
Indicates the current state or condition of two or more forces that are in conflict or opposition to each other.
-
C.
opposedWar
Indicates that an entity actively resisted, disagreed with, or worked against a particular war or military conflict.
-
D.
opposingAlliance
Indicates that two entities belong to rival or mutually opposed alliances or factions.
-
E.
engagedForces
Indicates that one force has actively committed or deployed its military units against another force in combat or operational interaction.
- 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_69c68a65402881908f7869368eb746fb |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f36d0fbc81908cb7cfe99f80de08 |
completed | March 27, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69c6f038582c8190bac77c9b5a34b862 |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f0be2b1c8190bea06100a7caef2b |
completed | March 27, 2026, 9:03 p.m. |
Created at: March 27, 2026, 3:13 p.m.