Triple
T35252259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waray Wiktionary |
E1018124
|
entity |
| Predicate | hasGlossaryType |
P182660
|
FINISHED |
| Object | multilingual dictionary with Waray interface |
—
|
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: multilingual dictionary with Waray interface | Statement: [Waray Wiktionary, hasGlossaryType, multilingual dictionary with Waray interface]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGlossaryType Context triple: [Waray Wiktionary, hasGlossaryType, multilingual dictionary with Waray interface]
-
A.
hasGlossary
Indicates that one entity includes or is associated with a glossary that defines terms or concepts related to it.
-
B.
hasGlossesBy
Indicates a relationship where one entity provides or is associated with explanatory glosses or definitions for another entity.
-
C.
hasGloss
Indicates that one entity provides a textual definition or explanatory gloss for another entity.
-
D.
hasGradeType
Indicates that an entity is associated with a particular category or type of grade (e.g., letter grade, pass/fail, percentage).
-
E.
hasCorpusType
Indicates the type or category of corpus associated with an entity (e.g., text, speech, multimodal).
- F. None of above. chosen
Provenance (4 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_69f76de407d081909dfc3c419817ae93 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7904a770481908ef3f788e51e8dba |
completed | May 3, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69f78e2d71248190b850c2802ec170c0 |
completed | May 3, 2026, 6:04 p.m. |
| PDg | Predicate description generation | batch_69f78f629d508190b755848162c4e101 |
completed | May 3, 2026, 6:09 p.m. |
Created at: May 3, 2026, 4:02 p.m.