Triple
T1492147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RDF |
E29602
|
entity |
| Predicate | relatedStandard |
P37
|
FINISHED |
| Object |
SHACL
SHACL is a W3C standard language for validating RDF data against a set of constraints or shapes.
|
E171301
|
NE FINISHED |
How this triple was built (4 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: SHACL | Statement: [RDF, relatedStandard, SHACL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SHACL Context triple: [RDF, relatedStandard, SHACL]
-
A.
OWL
OWL (Web Ontology Language) is a W3C-recommended semantic web language used to define and share rich, machine-interpretable ontologies on the web.
-
B.
SPARQL
SPARQL is a semantic query language and protocol used to retrieve and manipulate data stored in Resource Description Framework (RDF) format on the Semantic Web.
-
C.
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.
-
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 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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: SHACL Triple: [RDF, relatedStandard, SHACL]
Generated description
SHACL is a W3C standard language for validating RDF data against a set of constraints or shapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SHACL Target entity description: SHACL is a W3C standard language for validating RDF data against a set of constraints or shapes.
-
A.
OWL
OWL (Web Ontology Language) is a W3C-recommended semantic web language used to define and share rich, machine-interpretable ontologies on the web.
-
B.
SPARQL
SPARQL is a semantic query language and protocol used to retrieve and manipulate data stored in Resource Description Framework (RDF) format on the Semantic Web.
-
C.
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.
-
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 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.
- F. None of above. chosen
Provenance (5 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_69a498dba1d8819093b46a3a8d2485f1 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6c4f0c88190a97ba4910c1a5d85 |
completed | March 1, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad1ca98e64819097916eb7717e6364 |
completed | March 8, 2026, 6:52 a.m. |
| NEDg | Description generation | batch_69ad1d34656481909949b4bfd83c6142 |
completed | March 8, 2026, 6:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad1dd7b34c8190b6957be2112506dd |
completed | March 8, 2026, 6:57 a.m. |
Created at: March 1, 2026, 8:12 p.m.