Triple
T12598502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinook Wawa |
E300794
|
entity |
| Predicate | hasApproximateVocabularySize |
P67671
|
FINISHED |
| Object | few hundred words |
—
|
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: few hundred words | Statement: [Chinook Wawa, hasApproximateVocabularySize, few hundred words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateVocabularySize Context triple: [Chinook Wawa, hasApproximateVocabularySize, few hundred words]
-
A.
hasApproximateMemberCount
Indicates that an entity is associated with a group or collection for which only an estimated or non-exact number of members is known.
-
B.
hasApproximateEntryCount
Indicates that an entity is associated with a number representing an estimated or non-exact count of its entries.
-
C.
hasApproximateNumberOfSymbols
Indicates that an entity is associated with a quantity of symbols that is approximate rather than exact.
-
D.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
E.
hasApproximateNumberOfAttestedWords
chosen
Indicates that an entity is associated with an estimated or approximate count of words that are documented or attested for it.
- 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9559458dc8190bc4d6e697e99d70e |
completed | April 10, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69d9541894fc8190a0c3706a414279f0 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 9, 2026, 5:09 p.m.