Triple
T1919785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SKOS |
E40098
|
entity |
| Predicate | relatedStandard |
P37
|
FINISHED |
| Object | OWL |
E4407
|
NE 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: OWL | Statement: [SKOS, relatedStandard, OWL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OWL Context triple: [SKOS, relatedStandard, OWL]
-
A.
OWL
chosen
OWL (Web Ontology Language) is a W3C-recommended semantic web language used to define and share rich, machine-interpretable ontologies on the web.
-
B.
OWL DL
OWL DL is a sublanguage of the Web Ontology Language that balances expressive power with computational decidability by adhering closely to description logic foundations.
-
C.
OWL Full
OWL Full is the most expressive and semantically unrestricted variant of the Web Ontology Language, allowing full RDF compatibility at the cost of computational decidability.
-
D.
RDF
RDF (Resource Description Framework) is a standard model for data interchange on the Web that represents information as subject–predicate–object triples to enable structured, machine-readable metadata and knowledge graphs.
-
E.
OWL 2 QL
OWL 2 QL is a lightweight profile of the Web Ontology Language designed to enable efficient query answering over large datasets using standard relational database technologies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb213af0481909429ec971860a3fd |
completed | March 7, 2026, 5:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3df3fdc819095cfdf508e8bdb22 |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:35 p.m.