Triple
T16845869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cardiff Rift |
E409536
|
entity |
| Predicate | effectOnArea |
P1586
|
FINISHED |
| Object | attracts alien artifacts |
—
|
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: attracts alien artifacts | Statement: [Cardiff Rift, effectOnArea, attracts alien artifacts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnArea Context triple: [Cardiff Rift, effectOnArea, attracts alien artifacts]
-
A.
affectedArea
chosen
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
B.
placeOfEffect
Indicates the location or setting where an action, event, or effect takes place or is realized.
-
C.
areaOfSupport
Indicates the spatial region or domain within which an entity provides support, assistance, or backing to another.
-
D.
effectOnOthers
Indicates the impact or influence that one entity’s actions, presence, or state has on other entities.
-
E.
effectiveArea
Indicates the portion of a surface or region that actually contributes to a specified effect, such as performance, interaction, or impact, within a given context.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b3541a008190b2a97cfea92b170f |
completed | April 18, 2026, 4:37 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.