Triple
T4150635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scyphozoa |
E89894
|
entity |
| Predicate | stingEffectOnHumans |
P43904
|
FINISHED |
| Object | often painful but usually non-lethal |
—
|
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: often painful but usually non-lethal | Statement: [Scyphozoa, stingEffectOnHumans, often painful but usually non-lethal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stingEffectOnHumans Context triple: [Scyphozoa, stingEffectOnHumans, often painful but usually non-lethal]
-
A.
involvedPhysicalEffect
Indicates that one entity participates in causing, experiencing, or mediating a physical effect on another entity or the environment.
-
B.
impactOnHumans
chosen
Indicates a relationship where something produces an effect, influence, or consequence on humans.
-
C.
effectOfDeath
Indicates the causal impact or consequences that a death has on another entity, state, or process.
-
D.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
-
E.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
- 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_69aed95a59a881909b26e70b42c6811a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af033ef6648190adde17f943d89c78 |
completed | March 9, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69af018c101081909070da5b11e5eb3d |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:43 p.m.