Triple
T38644866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medic |
E938688
|
entity |
| Predicate | OpticalFlareEffect |
P16366
|
FINISHED |
| Object | reduces enemy sight range to minimum |
—
|
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: reduces enemy sight range to minimum | Statement: [Medic, OpticalFlareEffect, reduces enemy sight range to minimum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: OpticalFlareEffect Context triple: [Medic, OpticalFlareEffect, reduces enemy sight range to minimum]
-
A.
visualEffect
chosen
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
B.
fireEffect
Indicates that one entity produces, causes, or is associated with a fire-related impact or consequence on another entity.
-
C.
projectionEffect
Indicates the visual or spatial transformation produced when something is projected from one surface, medium, or viewpoint onto another.
-
D.
ashCloudEffect
Indicates the impact or consequences that an ash cloud has on other entities, conditions, or processes.
-
E.
hasLightingEffect
Indicates that one entity applies, produces, or is associated with a particular lighting effect on another entity or environment.
- 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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdb0de8c08190928cd1323f80ab5c |
completed | May 7, 2026, 6:33 p.m. |
| PD | Predicate disambiguation | batch_69fcd9017dd88190b32a73fe78909740 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.