Triple
T9529624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outstanding Morning Program |
E229852
|
entity |
| Predicate | languageOfAwardAdministration |
P88562
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Outstanding Morning Program, languageOfAwardAdministration, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfAwardAdministration Context triple: [Outstanding Morning Program, languageOfAwardAdministration, English]
-
A.
languageOfAwardingInstitution
Indicates the language in which the awarding institution formally grants or documents the award.
-
B.
historicallyDominantLanguageOfAdministrationIn
Indicates that a language has historically been the primary language used for official governance and administrative functions within a given place or political entity.
-
C.
awardNameLanguage
Indicates the language in which the name of an award is expressed.
-
D.
languageFamilyOfAdministration
Indicates the language family used as the primary medium of official governance or administrative functions for an entity.
-
E.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
- 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_69ca8479934c81908006d0e6e970ae05 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98b1b93481909812245ac14e4988 |
completed | April 1, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69cca56c44f88190a54a5d2a133bb07e |
completed | April 1, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69cca89f1d748190bf3636bea28d8a37 |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 8 p.m.