Triple
T2369049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fiordland crested penguin |
E46046
|
entity |
| Predicate | hasCommonNameInLanguage |
P21937
|
FINISHED |
| Object | tawaki (Māori) |
—
|
LITERAL 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: tawaki (Māori) | Statement: [Fiordland crested penguin, hasCommonNameInLanguage, tawaki (Māori)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonNameInLanguage Context triple: [Fiordland crested penguin, hasCommonNameInLanguage, tawaki (Māori)]
-
A.
hasNameInLocalLanguage
chosen
Indicates that an entity is associated with a name expressed in the local or native language of a given context or region.
-
B.
hasFamilyNameInLanguage
Indicates that an entity has a specific family name as expressed or written in a particular language.
-
C.
includesCommonName
Indicates that one entity contains or specifies a commonly used (non-scientific) name for another entity.
-
D.
hasEnglishName
Indicates that an entity is associated with a name expressed in the English language.
-
E.
commonNameOf
Indicates that one entity is the commonly used or popular name by which the other entity is known.
- F. None of above.
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_69a88a145268819083e2736cb835c696 |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abc76dcaa481908567a068bd61e5ad |
completed | March 7, 2026, 6:36 a.m. |
| PD | Predicate disambiguation | batch_69abc59b88348190a2d6c08f69974117 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:56 p.m.