Triple
T21448821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruth Levinson |
E529149
|
entity |
| Predicate | mentionedVia |
P144388
|
FINISHED |
| Object | letters |
—
|
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: letters | Statement: [Ruth Levinson, mentionedVia, letters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mentionedVia Context triple: [Ruth Levinson, mentionedVia, letters]
-
A.
mentionedWith
Indicates that two entities are mentioned together or in close association within the same context, such as a document, sentence, or conversation.
-
B.
mentionedAs
Indicates that one entity is referred to or cited by name or description in the context of another entity.
-
C.
mentionsSee
Indicates that one entity explicitly refers to or cites another entity within its content.
-
D.
mentions
Indicates that one entity refers to, cites, or brings up another entity in some form of communication or content.
-
E.
mentionedSince
Indicates that one entity has been mentioned or referred to continuously or at least once from a specified point in time onward.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d11ca48190aafe25c97dfa5578 |
completed | April 23, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69e631df1b38819088d3604854e697b4 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e63d2aca38819094d312078feaa436 |
completed | April 20, 2026, 2:50 p.m. |
Created at: April 16, 2026, 6:06 p.m.