Triple
T857984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herman J. Muller |
E18535
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Muller |
E35404
|
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: Muller | Statement: [Herman J. Muller, familyName, Muller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Muller Context triple: [Herman J. Muller, familyName, Muller]
-
A.
Müller
chosen
Müller is a common German surname, equivalent to "Miller" in English, historically associated with the occupation of operating a mill.
-
B.
Morgenstern
Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
-
C.
Ehrlich
Ehrlich is a German-origin surname borne by numerous notable individuals across fields such as science, medicine, and the arts.
-
D.
Ernst
Ernst is a masculine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
-
E.
Martz
Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac4f740881909cb59a6c18a77af3 |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3c1ee4481909d5713122e5ad856 |
completed | March 4, 2026, 3:15 a.m. |
Created at: March 1, 2026, 7:39 p.m.