Triple
T25939264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Falls of Cheat |
E653646
|
entity |
| Predicate | hasApproximateHeightInFeet |
P8302
|
FINISHED |
| Object | about 50 to 60 feet |
—
|
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 50 to 60 feet | Statement: [High Falls of Cheat, hasApproximateHeightInFeet, about 50 to 60 feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateHeightInFeet Context triple: [High Falls of Cheat, hasApproximateHeightInFeet, about 50 to 60 feet]
-
A.
heightApproximateFeet
chosen
Indicates that one entity’s height is approximately equal to a specified value measured in feet.
-
B.
heightFeet
Indicates a relationship where a subject has its vertical size or stature specified in feet as a unit of measurement.
-
C.
hasLengthFeet
Indicates that an entity possesses a length measured in feet.
-
D.
heightInInches
Indicates that one entity has a specific height measured in inches.
-
E.
hasApproxElevationFeet
Indicates that an entity is associated with an elevation value measured in feet that is approximate 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_69e7ab3fd2f881908837305e4ba98011 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6691f5e188190b12c7b2eb729a45e |
completed | May 2, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69f66598d6008190a7ca8ff80399fd34 |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 22, 2026, 8:40 a.m.