Triple
T6923079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Azuei |
E160233
|
entity |
| Predicate | approximateShape |
P40719
|
FINISHED |
| Object | elongated |
—
|
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: elongated | Statement: [Lake Azuei, approximateShape, elongated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateShape Context triple: [Lake Azuei, approximateShape, elongated]
-
A.
hasApproximateShape
chosen
Indicates that one entity has a shape that is similar to, but not exactly the same as, the shape of another entity.
-
B.
approximateSize
Indicates that one entity has a size that is roughly or approximately equal to the size of another entity.
-
C.
approximateRadius
Indicates that one entity specifies or provides an estimated value for the radius of another entity.
-
D.
hasDimensionsApprox
Indicates that an entity has physical dimensions that are known only approximately, rather than as exact measurements.
-
E.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
- 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_69c6884d350081908d8a970e4d40ad78 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9fd159c819092a69d1a24e22dd5 |
completed | March 27, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bb577c81908ee8b415b4281f3d |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:26 p.m.