Triple
T38620459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Affliction |
E936851
|
entity |
| Predicate | introducesArea |
P199553
|
FINISHED |
| Object | corrupted Viridian Wildwood |
—
|
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: corrupted Viridian Wildwood | Statement: [Affliction, introducesArea, corrupted Viridian Wildwood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: introducesArea Context triple: [Affliction, introducesArea, corrupted Viridian Wildwood]
-
A.
connectsToAreaKnownFor
Indicates that something has a direct link or association to a specific area that is recognized or notable for certain characteristics or features.
-
B.
relatedVenueArea
Indicates that a venue is associated with or located within a particular geographic or spatial area.
-
C.
areaComponent
Indicates that one area is a constituent or sub-area that forms part of a larger area.
-
D.
subArea
Indicates that one area is a constituent or contained part of a larger area.
-
E.
hasAreaName
Indicates that an entity is associated with a specific named geographic or administrative area.
- 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_69f76ed403208190b862dc795171353f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ff41645c548190b7cb4e53079b93ef |
completed | May 9, 2026, 2:15 p.m. |
| PD | Predicate disambiguation | batch_69ff410aa33c8190869ba769ac2a93ce |
completed | May 9, 2026, 2:13 p.m. |
| PDg | Predicate description generation | batch_69ff4163a8548190b0eaafd0a377b141 |
completed | May 9, 2026, 2:14 p.m. |
Created at: May 3, 2026, 4:32 p.m.