Triple
T1528541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kardashev scale |
E32388
|
entity |
| Predicate | originalNumberOfTypes |
P29856
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Kardashev scale, originalNumberOfTypes, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalNumberOfTypes Context triple: [Kardashev scale, originalNumberOfTypes, 3]
-
A.
originalNumberOfClasses
Indicates the initial total count of classes before any changes such as additions, removals, or merges occur.
-
B.
numberOfSpecies
Indicates the count of distinct species associated with a given entity or context.
-
C.
numberingType
Indicates the scheme or style used to assign sequential numbers or labels within an ordered set.
-
D.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
E.
originalNumberOfLeaves
Indicates the initial count of leaves associated with an entity before any changes, losses, or additions occur.
- F. None of above. chosen
Provenance (4 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a933ddc5a881909cdf503f2bc29bd4 |
completed | March 5, 2026, 7:42 a.m. |
| PD | Predicate disambiguation | batch_69a907ae8f688190ad9000ea1e018585 |
completed | March 5, 2026, 4:33 a.m. |
| PDg | Predicate description generation | batch_69a933dce3488190b20f0e3d37d16371 |
completed | March 5, 2026, 7:42 a.m. |
Created at: March 4, 2026, 7:26 p.m.