Triple
T38280708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syriac-speaking Near East |
E1022072
|
entity |
| Predicate | languageOfScientificLearning |
P203804
|
FINISHED |
| Object | Syriac |
E7978
|
NE 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: Syriac | Statement: [Syriac-speaking Near East, languageOfScientificLearning, Syriac]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfScientificLearning Context triple: [Syriac-speaking Near East, languageOfScientificLearning, Syriac]
-
A.
languageOfTeachings
Indicates the language in which teachings, lessons, or instructional content are delivered or expressed.
-
B.
languageOfSubjects
Indicates the language used by or associated with the subjects in question.
-
C.
languageBranchStudied
Indicates that a person studies or has studied a particular branch or subgroup of a language.
-
D.
learnsLanguage
Indicates that an entity acquires knowledge or skill in a particular language through study or practice.
-
E.
learnsLanguageFrom
Indicates that one entity acquires or improves knowledge of a language through instruction, exposure, or guidance provided by another entity.
- F. None of above. chosen
Provenance (5 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_69f76df0cddc81908d16c1556ff4097f |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0207a1272c81909dcdb6e3307702f5 |
completed | May 11, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41b28c413c8190a8e80869cc27f0ea |
completed | June 28, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_6a0205fa1e608190829fd2baa0434fff |
completed | May 11, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_6a0207a073508190adf36e94ab2380ad |
completed | May 11, 2026, 4:45 p.m. |
Created at: May 3, 2026, 4:30 p.m.