Triple
T24936289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monument Rocks (Kansas) |
E623320
|
entity |
| Predicate | approximateMaximumThicknessOfChalk |
P55323
|
FINISHED |
| Object | tens of meters |
—
|
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: tens of meters | Statement: [Monument Rocks (Kansas), approximateMaximumThicknessOfChalk, tens of meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateMaximumThicknessOfChalk Context triple: [Monument Rocks (Kansas), approximateMaximumThicknessOfChalk, tens of meters]
-
A.
hasThicknessRange
Indicates that an entity is associated with a minimum and maximum thickness value defining the range of its thickness.
-
B.
typicalPenAngle
Indicates the usual or characteristic angle at which a pen is held or positioned during use.
-
C.
hasMaximumThickness
chosen
Indicates that an entity possesses a specified upper limit on its thickness.
-
D.
canInk
Indicates that one entity has the capability or function to apply ink to another entity.
-
E.
typicalDrawWeight
Indicates the usual or standard amount of force required to draw a bow (or similar equipment) associated with an 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f62d89b89c8190afb372a8172111e7 |
completed | May 2, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69f62c1379f08190836c3e02b0c892df |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 18, 2026, 5:30 a.m.