Triple
T15419436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bay City (fictional) |
E369333
|
entity |
| Predicate | languageWithinSetting |
P18209
|
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: [Bay City (fictional), languageWithinSetting, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageWithinSetting Context triple: [Bay City (fictional), languageWithinSetting, English]
-
A.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
B.
languageSpecifies
Indicates that one entity defines or constrains the syntax, semantics, or usage rules that govern how another language or linguistic system is expressed or interpreted.
-
C.
languageUse
chosen
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
D.
languageModality
Indicates the mode or form in which a language is expressed or perceived (e.g., spoken, signed, written, or tactile).
-
E.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ebce4f48190ba282ecb4fb2f6fa |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded27f45548190a6d2b1b85cb47444 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:20 a.m.