Triple
T18266270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | noweb |
E437491
|
entity |
| Predicate | chunkingModel |
P17710
|
FINISHED |
| Object | named code chunks |
—
|
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: named code chunks | Statement: [noweb, chunkingModel, named code chunks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chunkingModel Context triple: [noweb, chunkingModel, named code chunks]
-
A.
segmentPositioning
Indicates how segments are arranged or ordered relative to one another within a larger structure or sequence.
-
B.
segmentStructure
chosen
Indicates that one entity represents a structural or organizational subdivision (a segment) within the overall structure of another entity.
-
C.
usesSubwordTokenization
Indicates that a text processing system represents sequences using subword units rather than whole words or characters.
-
D.
segmentation
Indicates dividing something into distinct parts or segments based on certain criteria or boundaries.
-
E.
divisionSize
Indicates the size or magnitude of a division or subdivided part in relation to a whole.
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ff7af85c81909859e7247738a535 |
completed | April 19, 2026, 4:14 p.m. |
| PD | Predicate disambiguation | batch_69e44fd81c788190b08c6be3b07a08c5 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:34 a.m.