Triple
T12680238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Survivor from Warsaw |
E302924
|
entity |
| Predicate | vocalLanguageDistribution |
P47965
|
FINISHED |
| Object | narration in 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: narration in English | Statement: [A Survivor from Warsaw, vocalLanguageDistribution, narration in English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vocalLanguageDistribution Context triple: [A Survivor from Warsaw, vocalLanguageDistribution, narration in English]
-
A.
languageOfVocalization
chosen
Indicates the language in which a vocalization (such as speech or singing) is produced.
-
B.
languageDiversity
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
-
C.
isSpokenAsFirstLanguageBy
Indicates that a language is the primary (native) language used by a person or group for everyday communication.
-
D.
isSpokenOn
Indicates that a particular language, phrase, or utterance is used or occurs during a specified time, event, or occasion.
-
E.
demographicsSignificantLanguage
Indicates that a particular language is significantly represented or prevalent within the demographic profile of a population or group.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961b32dbc81908101fc5f07e26ed3 |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:21 p.m.