Triple
T15787651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | STaM |
E382778
|
entity |
| Predicate | letterCountRules |
P41948
|
FINISHED |
| Object | fixed number of letters per word |
—
|
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: fixed number of letters per word | Statement: [STaM, letterCountRules, fixed number of letters per word]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: letterCountRules Context triple: [STaM, letterCountRules, fixed number of letters per word]
-
A.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
B.
hasStandardLetterCount
Indicates that an entity’s associated text or label contains a number of letters that matches a predefined standard or expected count.
-
C.
countingRule
chosen
Indicates the rule or method used to count or quantify items, events, or entities in a given context.
-
D.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0540380448190a025338f0e62e6d1 |
completed | April 16, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69e00537bd1c81908d6e832792fd934f |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:48 a.m.