Triple
T1326641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Middlebury College |
E28342
|
entity |
| Predicate | teachesLanguages |
P1253
|
FINISHED |
| Object | over 10 languages in Language Schools |
—
|
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: over 10 languages in Language Schools | Statement: [Middlebury College, teachesLanguages, over 10 languages in Language Schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachesLanguages Context triple: [Middlebury College, teachesLanguages, over 10 languages in Language Schools]
-
A.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
-
B.
taughtAsForeignLanguageIn
chosen
Indicates that a language is taught as a foreign (non-native) language within a particular educational context or institution.
-
C.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
D.
hasLanguageOfStudy
Indicates that an entity studies or is engaged in learning a particular language.
-
E.
taughtAs
Indicates that one entity served as a teacher or instructor for another entity in an educational or training context.
- 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_69a498540a2481909e807a762280d3ba |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1c0a22881909eff0fc6c91a5f41 |
completed | March 1, 2026, 10:46 p.m. |
| PD | Predicate disambiguation | batch_69a4beedb49c8190beb5b85cdda05013 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:55 p.m.