Triple
T30754147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ureshino Onsen |
E783033
|
entity |
| Predicate | reputedEffect |
P159359
|
FINISHED |
| Object | smooth skin |
—
|
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: smooth skin | Statement: [Ureshino Onsen, reputedEffect, smooth skin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reputedEffect Context triple: [Ureshino Onsen, reputedEffect, smooth skin]
-
A.
reputationEffect
Indicates how one entity’s actions or characteristics influence the perceived reputation or standing of another entity.
-
B.
canonicalEffect
chosen
Indicates the standard or primary effect that an action, event, or entity is typically understood to produce.
-
C.
recognitionEffectOn
Indicates the effect or consequence that an act of recognition by one entity has on another entity or state.
-
D.
notableEffect
Indicates that one entity has a significant impact, consequence, or influence on another entity or situation.
-
E.
providesEffect
Indicates that one entity causes, delivers, or produces a particular effect or outcome on another entity.
- 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_69f224af8d8481908bea03890c5618be |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68f93f8b48190aebe0bbbd662f07b |
completed | May 2, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:39 p.m.