Triple
T2020605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas–New York |
E44094
|
entity |
| Predicate | mediaNarrativeTheme |
P7324
|
FINISHED |
| Object | big state vs big city |
—
|
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: big state vs big city | Statement: [Texas–New York, mediaNarrativeTheme, big state vs big city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaNarrativeTheme Context triple: [Texas–New York, mediaNarrativeTheme, big state vs big city]
-
A.
theme
Indicates the entity that is the primary participant or content affected or characterized by an action, event, or state.
-
B.
themeFor
Indicates that something serves as the central subject, topic, or focus for another thing (such as an event, work, or activity).
-
C.
notableTheme
Indicates that a particular theme is prominently featured in, or strongly associated with, an entity such as a work, event, or body of content.
-
D.
narrativeFrame
chosen
Indicates the overarching narrative context or perspective within which events, actions, or relationships are presented or interpreted.
-
E.
narrativeType
Indicates the specific kind or category of narrative (e.g., genre, structural form, or storytelling mode) 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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8d0bcbc8190bbbb726ecae1c51b |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb7a389408190a84a54856352f15b |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:38 p.m.