Triple
T10930940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Plains Zoo |
E258202
|
entity |
| Predicate | hasAnimalSpeciesCount |
P6211
|
FINISHED |
| Object | more than 100 species |
—
|
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: more than 100 species | Statement: [Great Plains Zoo, hasAnimalSpeciesCount, more than 100 species]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAnimalSpeciesCount Context triple: [Great Plains Zoo, hasAnimalSpeciesCount, more than 100 species]
-
A.
numberOfSpecies
chosen
Indicates the count of distinct species associated with a given entity or context.
-
B.
speciesNumber
Indicates the numerical identifier or count associated with a particular species in a given context.
-
C.
numberOfAnimals
Indicates the quantity of animals associated with a given entity or context.
-
D.
hasMammalSpecies
Indicates that one entity includes, contains, or is associated with a particular mammal species as part of its composition, population, or classification.
-
E.
hasWildPopulationOf
Indicates that a location or area contains a naturally occurring, non-captive population of the specified species.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7709f92088190a15ae3638d3b14fb |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:22 p.m.