Triple
T21413757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boontling |
E528244
|
entity |
| Predicate | hasApproximateNumberOfWords |
P67671
|
FINISHED |
| Object | over 1000 |
—
|
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 1000 | Statement: [Boontling, hasApproximateNumberOfWords, over 1000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateNumberOfWords Context triple: [Boontling, hasApproximateNumberOfWords, over 1000]
-
A.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
B.
hasApproximateNumberOfAttestedWords
chosen
Indicates that an entity is associated with an estimated or approximate count of words that are documented or attested for it.
-
C.
hasApproximateNumberOfSymbols
Indicates that an entity is associated with a quantity of symbols that is approximate rather than exact.
-
D.
hasApproximateNumberOfGlosses
Indicates that an entity is associated with an estimated or non-exact count of glosses (explanatory notes or definitions).
-
E.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b201ce1481908392c77e5ca40f5f |
completed | April 22, 2026, 11:33 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:44 p.m.