Triple
T24239785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sma |
E603188
|
entity |
| Predicate | basedOnAcronymOf |
P8733
|
FINISHED |
| Object | Sefer Me’irat Einayim |
—
|
NE NERFINISHED |
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: Sefer Me’irat Einayim | Statement: [Sma, basedOnAcronymOf, Sefer Me’irat Einayim]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnAcronymOf Context triple: [Sma, basedOnAcronymOf, Sefer Me’irat Einayim]
-
A.
hasAcronymOrigin
chosen
Indicates that an acronym is derived from or originates from a specific longer expression or name.
-
B.
acronymOfNativeName
Indicates that one term is an acronym formed from the native-language version of another name.
-
C.
organizationAcronym
Indicates that an organization is known or referred to by a specific acronym.
-
D.
acronymContext
Indicates that one term is used as an acronym specifically within the contextual scope defined by another entity (such as a document, domain, or usage setting).
-
E.
abbreviationUsedBy
Indicates that a particular abbreviation is employed or adopted by a specified entity in its communication, documentation, or usage.
- 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_69e2953f631c819097cbb421046bd417 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28a9eb68c81908a8293c00e581b41 |
completed | April 29, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f1c448abec8190b87cbf9ed419a309 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:03 a.m.