Triple
T18672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Latin alphabet |
E368
|
entity |
| Predicate | hasStandardLetterCount |
P1440
|
FINISHED |
| Object | 26 letters in modern English usage |
—
|
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: 26 letters in modern English usage | Statement: [Latin alphabet, hasStandardLetterCount, 26 letters in modern English usage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStandardLetterCount Context triple: [Latin alphabet, hasStandardLetterCount, 26 letters in modern English usage]
-
A.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
B.
hasStandardizationBody
Indicates that an entity is associated with, governed by, or defined by a specific standards-setting organization or authority.
-
C.
numberOfSpans
Indicates the total count of distinct spans or segments associated with an entity or within a specified context.
-
D.
typicalHeight
Indicates the usual or characteristic height associated with an entity, such as a person, object, or species.
-
E.
hasBasicWordOrder
Indicates the typical sequence in which core sentence elements (such as subject, verb, and object) are ordered in a language.
- 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_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a246cbca108190a92478df126d9bf8 |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a2464f61648190ac690044be194972 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a246cb2904819085c13207565a1db2 |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.