Triple
T32053788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macon television market |
E818560
|
entity |
| Predicate | secondaryCityOfMarket |
P48850
|
FINISHED |
| Object | Warner Robins, Georgia |
—
|
NE NERFINISHED |
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: Warner Robins, Georgia | Statement: [Macon television market, secondaryCityOfMarket, Warner Robins, Georgia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryCityOfMarket Context triple: [Macon television market, secondaryCityOfMarket, Warner Robins, Georgia]
-
A.
secondaryCityTerminus
Indicates that a city serves as a secondary (non-primary) terminus or endpoint for a given route or line.
-
B.
secondMetropolitan
Indicates that one entity is the second metropolitan (e.g., second-ranking or second-designated metropolitan authority or see) in relation to another entity.
-
C.
secondaryCapital
Indicates that a location serves as an additional or secondary capital city for a political entity, alongside its primary capital.
-
D.
hasSecondaryCity
chosen
Indicates that an entity possesses or is associated with a secondary city in addition to its primary city.
-
E.
secondarySeeCity
Indicates that an entity has a secondary or less prominent association with a particular city.
- 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_69f348fdacec8190b9f74375ca3b2094 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe744faca881908e11e90e0a35653f |
completed | May 8, 2026, 11:39 p.m. |
| PD | Predicate disambiguation | batch_69fe734cbf7081909a552c5cf3b5ea59 |
completed | May 8, 2026, 11:35 p.m. |
Created at: May 1, 2026, 12:21 a.m.