Triple
T7115446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OWL 2 recommendation |
E165806
|
entity |
| Predicate | defines |
P264
|
FINISHED |
| Object | OWL 2 DL |
E643834
|
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 2 DL | Statement: [OWL 2 recommendation, defines, OWL 2 DL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OWL 2 DL Context triple: [OWL 2 recommendation, defines, OWL 2 DL]
-
A.
OWL 2 EL
OWL 2 EL is a lightweight profile of the Web Ontology Language designed for efficient reasoning over large-scale ontologies, particularly in domains like biomedical terminologies.
-
B.
OWL 2 Web Ontology Language
chosen
OWL 2 Web Ontology Language is a W3C-standardized knowledge representation language used to create, share, and reason over rich ontologies on the Semantic Web.
-
C.
OWL 2 RL
OWL 2 RL is a profile of the Web Ontology Language designed for scalable reasoning using rule-based systems, enabling efficient inference over large datasets.
-
D.
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.
-
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_69c6888227bc8190a1394679e3116f90 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e5f401b881909ef4c2ab1e0750db |
completed | March 27, 2026, 8:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7b8d92fec8190bd275023bdef8e08 |
completed | March 28, 2026, 11:17 a.m. |
Created at: March 27, 2026, 2:43 p.m.