Triple
T27889402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Words for Little People |
E705311
|
entity |
| Predicate | teachesVocabulary |
P21344
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Big Words for Little People, teachesVocabulary, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachesVocabulary Context triple: [Big Words for Little People, teachesVocabulary, true]
-
A.
teachesAbout
chosen
Indicates that one entity provides instruction or information to another entity on a particular subject or topic.
-
B.
learnedIn
Indicates that an entity acquired knowledge, skills, or information within a particular context, environment, or source.
-
C.
containsTeachingOf
Indicates that one entity includes, embodies, or presents the teaching, doctrine, or instructional content associated with another entity.
-
D.
languageOfTeachings
Indicates the language in which teachings, lessons, or instructional content are delivered or expressed.
-
E.
learnsLanguageFrom
Indicates that one entity acquires or improves knowledge of a language through instruction, exposure, or guidance provided by another entity.
- 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_69ef96b39c448190a9b3aa6672a5168f |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f6617ba4a88190bfc5c305acb4f93f |
completed | May 2, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69f660f082508190a95a7888ad66cb2e |
completed | May 2, 2026, 8:39 p.m. |
Created at: April 27, 2026, 6:35 p.m.