Triple
T16731838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faridabad NIT |
E406609
|
entity |
| Predicate | secondaryLanguageInRegion |
P10892
|
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: [Faridabad NIT, secondaryLanguageInRegion, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondaryLanguageInRegion Context triple: [Faridabad NIT, secondaryLanguageInRegion, English]
-
A.
laterSecondaryLanguageOfAdministration
Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another language.
-
B.
hasSecondaryNationalLanguage
Indicates that an entity possesses an officially recognized secondary national language in addition to its primary national language.
-
C.
regionLanguage
chosen
Indicates that a particular language is used or officially recognized within a specific geographic region.
-
D.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
E.
secondaryLanguageContext
Indicates that the associated information, interaction, or content occurs within or is tailored to a secondary (non-primary) language setting or usage context.
- 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_69d8838f242881908abd8bc138795886 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e39c362bb88190921fab43d76c3ee8 |
completed | April 18, 2026, 2:59 p.m. |
| PD | Predicate disambiguation | batch_69e319c807788190901250ab6e0ca55f |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:20 a.m.