Triple
T37614423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Accipiter rufitorques |
E935877
|
entity |
| Predicate | hasLatinSpeciesEpithet |
P198116
|
FINISHED |
| Object | rufitorques |
—
|
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: rufitorques | Statement: [Accipiter rufitorques, hasLatinSpeciesEpithet, rufitorques]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLatinSpeciesEpithet Context triple: [Accipiter rufitorques, hasLatinSpeciesEpithet, rufitorques]
-
A.
hasLatinName
Indicates that an entity is associated with a specific Latin (scientific) name.
-
B.
hasLatinNameMeaning
Indicates that an entity’s Latin name conveys a specific meaning or refers to a particular concept.
-
C.
hasLatinizedName
Indicates that an entity is associated with a version of its name that has been converted into Latin form or spelling.
-
D.
hasLatinTerm
chosen
Indicates that one entity is associated with a specific term expressed in Latin.
-
E.
hasGenusEpithet
Indicates that an organism or taxonomic name is associated with a specific genus epithet in its scientific classification.
- 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_69f76ed16b748190ad6add183b1be688 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffe6e2eb688190a45fd2c6415cd86a |
completed | May 10, 2026, 2:01 a.m. |
| PD | Predicate disambiguation | batch_69ffe65939488190a35b9c2e9c7ad868 |
completed | May 10, 2026, 1:58 a.m. |
Created at: May 3, 2026, 4:18 p.m.