Triple
T9243428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ziradei, Saran district, Bengal Presidency, British India |
E222122
|
entity |
| Predicate | hasSecondaryLanguageSpoken |
P27353
|
FINISHED |
| Object | Hindi |
—
|
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: Hindi | Statement: [Ziradei, Saran district, Bengal Presidency, British India, hasSecondaryLanguageSpoken, Hindi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecondaryLanguageSpoken Context triple: [Ziradei, Saran district, Bengal Presidency, British India, hasSecondaryLanguageSpoken, Hindi]
-
A.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
B.
hasSecondaryLanguageTradition
Indicates that an entity possesses an additional, non-primary language tradition associated with it, such as in its use, documentation, or cultural context.
-
C.
secondLanguageSpeakers
chosen
Indicates that the referenced language is spoken as a second (non-native) language by the specified group or number of people.
-
D.
hasPrimaryLanguage1
Indicates that an entity’s main or most commonly used language is the specified language.
-
E.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct 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_69ca83ee26cc81909ac624e190597d6d |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cd03edd37481908ea2f6dac354f04f |
completed | April 1, 2026, 11:39 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4765648190aa9445c4a22dc471 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:30 p.m.