Triple
T19703638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ParserFunctions |
E473155
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | Wikibooks |
—
|
NE NERFINISHED |
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: Wikibooks | Statement: [ParserFunctions, usedBy, Wikibooks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wikibooks Context triple: [ParserFunctions, usedBy, Wikibooks]
-
A.
Wikibooks
chosen
Wikibooks is a Wikimedia Foundation project that hosts collaboratively written, free-content textbooks and instructional guides.
-
B.
English Wikibooks
English Wikibooks is a Wikimedia Foundation project that hosts collaboratively written, open-content textbooks and instructional guides in English.
-
C.
Wikisource
Wikisource is a free online digital library of public domain and freely licensed texts that anyone can read and help transcribe.
-
D.
Japanese Wikibooks
Japanese Wikibooks is the Japanese-language edition of Wikibooks, a Wikimedia Foundation project that hosts collaboratively written open-content textbooks and instructional materials.
-
E.
Wikiversity
Wikiversity is a Wikimedia Foundation project that provides a free, collaborative platform for creating and using educational resources and learning materials.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e516dd048190a0b6c93ea3e71f58 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642b8707081908fbf96c989d2d52d |
completed | April 20, 2026, 3:14 p.m. |
Created at: April 10, 2026, 1:46 p.m.