Triple
T160037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Diego Zoo |
E3261
|
entity |
| Predicate | typeOfHabitatDesign |
P853
|
FINISHED |
| Object | open-air habitats |
—
|
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: open-air habitats | Statement: [San Diego Zoo, typeOfHabitatDesign, open-air habitats]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfHabitatDesign Context triple: [San Diego Zoo, typeOfHabitatDesign, open-air habitats]
-
A.
environmentType
chosen
Indicates the kind or category of environment associated with an entity or situation.
-
B.
architectureType
Indicates the specific style or category of architecture that characterizes or defines an entity.
-
C.
buildingType
Indicates the specific category or function that characterizes what kind of building something is.
-
D.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
E.
hasNatureDesignation
Indicates that something has been formally assigned a specific conservation, protection, or natural-area status or designation.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a25855baf48190a1b63f2e5865d957 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256623704819089d9eeefe05858ce |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.