Triple
T34706753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Persian Lessons |
E1000525
|
entity |
| Predicate | hasFakeLanguage |
P195201
|
FINISHED |
| Object | invented Persian-like language |
—
|
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: invented Persian-like language | Statement: [Persian Lessons, hasFakeLanguage, invented Persian-like language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFakeLanguage Context triple: [Persian Lessons, hasFakeLanguage, invented Persian-like language]
-
A.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
B.
hasLanguageOfSide
Indicates that an entity uses or is associated with a particular language on a specific side or aspect (e.g., one side of a bilingual object or interface).
-
C.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
D.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
-
E.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
- F. None of above. chosen
Provenance (4 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_69f76dab937881909c86f1b9ad50445f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69fdb04ed81c8190b8feea90c1c785a6 |
completed | May 8, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69fda9d6c5148190a63205b6d9b0a1b4 |
completed | May 8, 2026, 9:16 a.m. |
| PDg | Predicate description generation | batch_69fdb04d37288190969a0f39d92efb49 |
completed | May 8, 2026, 9:43 a.m. |
Created at: May 3, 2026, 3:59 p.m.