Triple
T37642948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MOR Philippines |
E936660
|
entity |
| Predicate | broadcastLanguagePolicy |
P73903
|
FINISHED |
| Object | mix of Filipino and 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: mix of Filipino and English | Statement: [MOR Philippines, broadcastLanguagePolicy, mix of Filipino and English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: broadcastLanguagePolicy Context triple: [MOR Philippines, broadcastLanguagePolicy, mix of Filipino and English]
-
A.
broadcastInLanguage
Indicates that a broadcast, program, or media content is transmitted or made available in a specified language.
-
B.
broadcastPolicy
Indicates that an entity defines or is governed by a specific set of rules or conditions for how information or content is broadcast or disseminated.
-
C.
shareLanguagePolicyCooperation
Indicates that two or more entities cooperate or coordinate with each other specifically on matters related to language policy.
-
D.
languagePolicyType
chosen
Indicates the specific category or type of language policy that governs how languages are used, managed, or regulated in a given context.
-
E.
languagePolicyAspect
Indicates an aspect or component of a broader language policy, such as its goals, rules, or implementation measures.
- 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_69f76ed31d8881908405da6c6d2f0463 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbb9e8108c8190ae1c7940b1677e95 |
completed | May 6, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fbb141605c8190b9c27d70352522db |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:18 p.m.