Triple
T22593342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meyer Levin |
E565006
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Meyer |
—
|
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: Meyer | Statement: [Meyer Levin, givenName, Meyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meyer Context triple: [Meyer Levin, givenName, Meyer]
-
A.
Meyer
Meyer is a common German-origin surname borne by numerous notable individuals across fields such as literature, entertainment, sports, and academia.
-
B.
Meyer
chosen
Meyer is a given name most famously associated with Meyer Lansky, a major organized crime figure in the United States during the 20th century.
-
C.
Meyer Meyer
Meyer Meyer is a fictional detective and key member of the 87th Precinct in Ed McBain’s long-running police procedural novel series.
-
D.
Meier
Meier is a common German surname borne by numerous individuals across various professions and regions.
-
E.
Meyerhof
Meyerhof is a surname of German origin, notably borne by biochemist Otto Fritz Meyerhof, a Nobel laureate recognized for his work on muscle metabolism.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245836014819091b91ed3074742a3 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f16163cb248190b377b110d80a6730 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 2:49 p.m.