Triple
T800827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DeFuniak Springs, Florida |
E17122
|
entity |
| Predicate | lakeShape |
P18842
|
FINISHED |
| Object | nearly perfectly circular |
—
|
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: nearly perfectly circular | Statement: [DeFuniak Springs, Florida, lakeShape, nearly perfectly circular]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lakeShape Context triple: [DeFuniak Springs, Florida, lakeShape, nearly perfectly circular]
-
A.
waterbodyType
Indicates the classification of a water body according to its type (e.g., river, lake, ocean, etc.).
-
B.
bodyOfWater
Indicates that one entity is a body of water that is geographically or physically associated with another entity.
-
C.
largestLakeEntirelyIn
Indicates that one entity is the largest lake located entirely within the boundaries of another entity (such as a country, region, or territory).
-
D.
riverFeatureType
Indicates the specific kind or category of physical or functional feature associated with a river (e.g., source, mouth, tributary, channel segment).
-
E.
hasMajorLake
Indicates that a geographic region or area contains at least one significant lake within its boundaries.
- F. None of above. chosen
Provenance (4 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7cc75e88190bd35aabe51051b51 |
completed | March 1, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69a4a5133bf88190a613e96d1f7cffa7 |
completed | March 1, 2026, 8:44 p.m. |
| PDg | Predicate description generation | batch_69a4a58c0a84819094f07658dc651b36 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:38 p.m.