Triple
T11463824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thinline |
E271726
|
entity |
| Predicate | hasRelativeThickness |
P8238
|
FINISHED |
| Object | thinner than standard Bible editions |
—
|
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: thinner than standard Bible editions | Statement: [Thinline, hasRelativeThickness, thinner than standard Bible editions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelativeThickness Context triple: [Thinline, hasRelativeThickness, thinner than standard Bible editions]
-
A.
hasMaximumThickness
Indicates that an entity possesses a specified upper limit on its thickness.
-
B.
hasRelativeSize
chosen
Indicates that one entity’s size is being compared to another entity’s size, expressing a relative rather than absolute magnitude.
-
C.
isThickenedWith
Indicates that one substance has been made more viscous or dense by adding another substance that serves as a thickening agent.
-
D.
thickness
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
E.
typicalThicknessFormula
Indicates the standard or commonly used formula for calculating the thickness of something under typical conditions.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f488248190b9f603cd31c72174 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:35 p.m.