Triple
T30943633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Cities of America |
E788325
|
entity |
| Predicate | languageUsedInFiction |
P43064
|
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: [United Cities of America, languageUsedInFiction, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageUsedInFiction Context triple: [United Cities of America, languageUsedInFiction, English]
-
A.
languageWithinFiction
Indicates that a language is used or exists within the context of a fictional work or fictional universe.
-
B.
fictionalUniverseLanguage
Indicates that a language is used or exists within a particular fictional universe.
-
C.
languageWrittenAbout
Indicates that something is written about or concerning a particular language.
-
D.
languageOfFictionalUniverse
chosen
Indicates the language used or spoken within a fictional universe or setting.
-
E.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
- 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_69f224c180f88190ad177372ee02b7e2 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ff56ef0a5c8190ae729d66a8cf7fc4 |
completed | May 9, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ff539859c481909ec56310da418688 |
completed | May 9, 2026, 3:32 p.m. |
Created at: April 29, 2026, 8:53 p.m.