Triple
T2534608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GMA Network |
E56238
|
entity |
| Predicate | typeOfChannel |
P8080
|
FINISHED |
| Object | commercial television network |
—
|
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: commercial television network | Statement: [GMA Network, typeOfChannel, commercial television network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfChannel Context triple: [GMA Network, typeOfChannel, commercial television network]
-
A.
typeOf
Indicates that one entity is a specific kind, class, or category instance of another more general entity.
-
B.
hasChannel
chosen
Indicates that one entity possesses, provides, or is associated with a particular communication or distribution channel.
-
C.
slotType
Indicates the classification or category assigned to a particular slot or position within a structure, system, or sequence.
-
D.
typeOfCondition
Indicates that one condition is a specific kind, category, or subtype of another condition.
-
E.
tunnelType
Indicates the specific kind or classification of a tunnel associated with an 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_69ab4a49b6508190bc467fbef4bac334 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd64a2194819097c66cbeb37fe859 |
completed | March 7, 2026, 7:39 a.m. |
| PD | Predicate disambiguation | batch_69abd0c4a5dc819097812db50443420a |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:47 p.m.