Triple
T5065761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onllwyn |
E114139
|
entity |
| Predicate | hasPostIndustrialCharacter |
P61054
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Onllwyn, hasPostIndustrialCharacter, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPostIndustrialCharacter Context triple: [Onllwyn, hasPostIndustrialCharacter, yes]
-
A.
hasIndustrialDevelopment
Indicates that an entity possesses, supports, or is characterized by industrial growth, infrastructure, or manufacturing-related development.
-
B.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
-
C.
hasIndustrialTown
Indicates that an entity possesses or is associated with a town characterized primarily by industrial activities or facilities.
-
D.
industrializes
Indicates the process by which an entity transforms its economy or operations from primarily agrarian or manual production to large-scale, mechanized, and industrial production.
-
E.
hasIndustrialAreaType
Indicates that an entity’s industrial area is classified as a specific type or category of industrial zone.
- 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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd749aceac8190817278266308fd64 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd715622b48190a3e8e49a5ef62b4a |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd738ac2e0819099c06cdcc5e21d28 |
completed | March 20, 2026, 4:19 p.m. |
Created at: March 20, 2026, 1:38 p.m.