Triple
T15385429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skellig Michael |
E367903
|
entity |
| Predicate | tourismImpactConcern |
P118568
|
FINISHED |
| Object | erosion of steps and structures |
—
|
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: erosion of steps and structures | Statement: [Skellig Michael, tourismImpactConcern, erosion of steps and structures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismImpactConcern Context triple: [Skellig Michael, tourismImpactConcern, erosion of steps and structures]
-
A.
hasTourismImpactOn
Indicates that one entity affects or influences the tourism levels, patterns, or attractiveness of another entity.
-
B.
tourismImportance
Indicates the degree to which a place or entity is significant or valuable as a destination or attraction for tourists.
-
C.
tourismAttitude
Indicates the stance, perception, or disposition that an entity holds toward tourism or tourist activities.
-
D.
hadTourismEconomyFor
Indicates that an entity has sustained a tourism-based economy for a specified period or duration.
-
E.
conservationImpact
Indicates the effect that an action, policy, or entity has on the preservation, protection, or restoration of natural ecosystems and biodiversity.
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e74ff70819094c1a85f51d6e228 |
completed | April 16, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69ded27742a881909cd73cc5c7d062fd |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded57005608190886cd01f640dfedb |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 3:19 a.m.