Triple
T7423374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SHACL |
E171301
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object |
ShEx
ShEx (Shape Expressions) is a concise, formal language for describing and validating the structure of RDF data graphs.
|
E664417
|
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: ShEx | Statement: [SHACL, relatedTo, ShEx]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ShEx Context triple: [SHACL, relatedTo, ShEx]
-
A.
SHACL
SHACL is a W3C standard language for validating RDF data against a set of constraints or shapes.
-
B.
OWL 2 Manchester syntax
OWL 2 Manchester syntax is a user-friendly, human-readable syntax for writing OWL 2 ontologies, designed to be easier to read and write than XML- or logic-based notations.
-
C.
OWL 2 functional-style syntax
OWL 2 functional-style syntax is a formal, logic-oriented textual notation for writing OWL 2 ontologies in a precise and machine-readable way.
-
D.
Schematron
Schematron is a rule-based XML schema language that uses XPath expressions to define and validate complex structural and business constraints in XML documents.
-
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. 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: ShEx Triple: [SHACL, relatedTo, ShEx]
Generated description
ShEx (Shape Expressions) is a concise, formal language for describing and validating the structure of RDF data graphs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ShEx Target entity description: ShEx (Shape Expressions) is a concise, formal language for describing and validating the structure of RDF data graphs.
-
A.
SHACL
SHACL is a W3C standard language for validating RDF data against a set of constraints or shapes.
-
B.
OWL 2 Manchester syntax
OWL 2 Manchester syntax is a user-friendly, human-readable syntax for writing OWL 2 ontologies, designed to be easier to read and write than XML- or logic-based notations.
-
C.
OWL 2 functional-style syntax
OWL 2 functional-style syntax is a formal, logic-oriented textual notation for writing OWL 2 ontologies in a precise and machine-readable way.
-
D.
Schematron
Schematron is a rule-based XML schema language that uses XPath expressions to define and validate complex structural and business constraints in XML documents.
-
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. 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_69c68a625d048190af70eb8b63bec5a0 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2eece588190905774e7151edcb8 |
completed | March 27, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81effc488819086336eea92604fa8 |
completed | March 28, 2026, 6:33 p.m. |
| NEDg | Description generation | batch_69c81fe025d081909f2a5c4515c60f64 |
completed | March 28, 2026, 6:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c824010104819081977e89d79ebb44 |
completed | March 28, 2026, 6:54 p.m. |
Created at: March 27, 2026, 3:12 p.m.