Triple
T2851922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaudesir |
E63111
|
entity |
| Predicate | hasExposure |
P15885
|
FINISHED |
| Object | mainly south-facing |
—
|
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: mainly south-facing | Statement: [Vaudesir, hasExposure, mainly south-facing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExposure Context triple: [Vaudesir, hasExposure, mainly south-facing]
-
A.
notableExposure
Indicates that an entity has received significant public attention, visibility, or coverage, making it notably exposed or recognized.
-
B.
hasSee
Indicates that one entity has perceived or visually observed another entity.
-
C.
exposureType
chosen
Indicates the specific manner or context in which one entity is exposed to another entity, condition, or influence.
-
D.
exposed
Indicates that one entity has been made visible, revealed, or left unprotected to another entity or to some external influence.
-
E.
hasHad
Indicates that an entity previously experienced, possessed, or was involved in something at some point in the past.
- 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf5ca2648190bd32c6ec4b0dd3b6 |
completed | March 7, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69abdd10aef88190b750aae07e7df4dc |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.