Triple
T4289903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake McKenzie |
E97362
|
entity |
| Predicate | hasApproxWidth |
P13004
|
FINISHED |
| Object | about 930 metres |
—
|
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: about 930 metres | Statement: [Lake McKenzie, hasApproxWidth, about 930 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproxWidth Context triple: [Lake McKenzie, hasApproxWidth, about 930 metres]
-
A.
hasApproximateMaximumWidth
chosen
Indicates that an entity’s maximum width is known only approximately, rather than as an exact value.
-
B.
hasWidth
Indicates that an entity possesses a specific measurement or extent along its width dimension.
-
C.
hasDimensionsApprox
Indicates that an entity has physical dimensions that are known only approximately, rather than as exact measurements.
-
D.
hasMaxLengthApprox
Indicates that something has a maximum length that is approximately equal to a specified value, allowing for some tolerance or imprecision.
-
E.
hasApproximateExtent
Indicates that one entity has a spatial, temporal, or quantitative extent that is only roughly or approximately specified rather than exact.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35061f5448190b3356b29a9129160 |
completed | March 12, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69b347fc4c0c8190a7fcd814e27308a5 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:08 p.m.