Triple
T4811722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villarrica National Park |
E107082
|
entity |
| Predicate | hasFauna |
P950
|
FINISHED |
| Object | kodkod |
E367964
|
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: kodkod | Statement: [Villarrica National Park, hasFauna, kodkod]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: kodkod Context triple: [Villarrica National Park, hasFauna, kodkod]
-
A.
kodkod
chosen
The kodkod is a small, elusive wild cat native to the temperate rainforests of southern Chile and Argentina, known for being one of the smallest felids in the Americas.
-
B.
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.
-
C.
TLA+
TLA+ is a formal specification language developed by Leslie Lamport for modeling and verifying concurrent and distributed systems using mathematical logic.
-
D.
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.
-
E.
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.
- 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_69bd43f779448190b92885cb70abb6c2 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6c7d168481908efd9d28b35e4bae |
completed | March 20, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be4dae0008819089c54a3815e578bc |
completed | March 21, 2026, 7:50 a.m. |
Created at: March 20, 2026, 1:23 p.m.