Triple
T7750562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meir |
E175747
|
entity |
| Predicate | variantForm |
P4680
|
FINISHED |
| Object | Meyer |
E345534
|
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: Meyer | Statement: [Meir, variantForm, Meyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meyer Context triple: [Meir, variantForm, Meyer]
-
A.
Meyer
Meyer is a given name most famously associated with Meyer Lansky, a major organized crime figure in the United States during the 20th century.
-
B.
Meyer
chosen
Meyer is a common German-origin surname borne by numerous notable individuals across fields such as literature, entertainment, sports, and academia.
-
C.
Meier
Meier is a common German surname borne by numerous individuals across various professions and regions.
-
D.
Mayer
Mayer is a common German-origin surname borne by numerous notable individuals across fields such as music, science, and politics.
-
E.
Meyers
Meyers is a surname shared by various individuals, including members of the wealthy French Bettencourt family.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c69960b3588190a53aa590d31d9544 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703b264c0819095c37534a676531d |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be576274819092e5ebdbcf2361da |
completed | March 29, 2026, 5:53 a.m. |
Created at: March 27, 2026, 4:08 p.m.