Triple
T30593645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woccon language |
E778727
|
entity |
| Predicate | numberOfRecordedWords |
P67671
|
FINISHED |
| Object | about 140 |
—
|
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: about 140 | Statement: [Woccon language, numberOfRecordedWords, about 140]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRecordedWords Context triple: [Woccon language, numberOfRecordedWords, about 140]
-
A.
numberOfKnownRecordings
Indicates the total count of recordings of an entity that are currently known or documented.
-
B.
phonemeInventorySize
Indicates the number of distinct phonemes present in a language’s sound system.
-
C.
hasApproximateNumberOfAttestedWords
chosen
Indicates that an entity is associated with an estimated or approximate count of words that are documented or attested for it.
-
D.
numberOfTrainingCommands
Indicates the total count of training commands or instructions associated with an entity or process.
-
E.
articulationCount
Indicates the number of distinct articulations or jointed connections present between the related entities.
- 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_69f224a1570c8190a85d3ac330479a79 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a00868083b081909afc3d8d4ad56b43 |
completed | May 10, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_6a0084f5f72c8190b08afa82690e322a |
completed | May 10, 2026, 1:15 p.m. |
Created at: April 29, 2026, 8:24 p.m.