Triple
T1938978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khmer script |
E41507
|
entity |
| Predicate | hasCharacterCount |
P32078
|
FINISHED |
| Object | over 70 letters including consonants and vowels |
—
|
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: over 70 letters including consonants and vowels | Statement: [Khmer script, hasCharacterCount, over 70 letters including consonants and vowels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacterCount Context triple: [Khmer script, hasCharacterCount, over 70 letters including consonants and vowels]
-
A.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
B.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
C.
numberOfCharacters
chosen
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
D.
hasStandardLetterCount
Indicates that an entity’s associated text or label contains a number of letters that matches a predefined standard or expected count.
-
E.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
- 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_69a88649b24c819080047f26b6db2ded |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb2c8be648190836580cec77a143f |
completed | March 7, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69abaff07cf88190b4883c5f17f90abd |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:36 p.m.