Triple
T5401428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daily Bugle newsroom |
E120785
|
entity |
| Predicate | languageOfFictionalSetting |
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: [Daily Bugle newsroom, languageOfFictionalSetting, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfFictionalSetting Context triple: [Daily Bugle newsroom, languageOfFictionalSetting, English]
-
A.
languageOfFictionalUniverse
chosen
Indicates the language used or spoken within a fictional universe or setting.
-
B.
areSpokenIn
Indicates that a particular language is used as a spoken means of communication within a specified region, community, or context.
-
C.
languageOfWritings
Indicates that a specified language is the one in which certain writings or written works are composed.
-
D.
typicalLanguages
Indicates the languages that are commonly or characteristically used, spoken, or associated with a given entity.
-
E.
macrolanguageOf
Indicates that one language functions as a macrolanguage encompassing or grouping together one or more related individual languages.
- 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_69bd46391c0c81909fa484446732b6a3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8932b8bc8190bd31e11b167a7212 |
completed | March 20, 2026, 5:51 p.m. |
| PD | Predicate disambiguation | batch_69bd84660ea08190a641084814fcf94d |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:04 p.m.