Triple
T651290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judeo-Persian |
E11349
|
entity |
| Predicate | containsLexicalElementsFrom |
P11236
|
FINISHED |
| Object | Hebrew |
—
|
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: Hebrew | Statement: [Judeo-Persian, containsLexicalElementsFrom, Hebrew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsLexicalElementsFrom Context triple: [Judeo-Persian, containsLexicalElementsFrom, Hebrew]
-
A.
hasLinguisticElement
Indicates that one entity includes, is associated with, or is characterized by a particular linguistic component such as a word, phrase, symbol, or other language element.
-
B.
hasLexicalInfluenceOn
Indicates that one linguistic element (such as a word, phrase, or lexicon) has affected or shaped the form, usage, or meaning of another linguistic element.
-
C.
hasLexicalSimilarityWith
Indicates that two linguistic items share a significant degree of similarity in form, structure, or wording.
-
D.
includesElement
chosen
Indicates that one collection, set, or structure contains a specified element as a member or component.
-
E.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f33b6d881908b6662b73d6fe833 |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d0eade081909c47e85ed55f808d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.