Triple
T17044656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philipp Matthäus Hahn |
E413534
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hahn |
E172540
|
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: Hahn | Statement: [Philipp Matthäus Hahn, familyName, Hahn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hahn Context triple: [Philipp Matthäus Hahn, familyName, Hahn]
-
A.
Hahn
chosen
Hahn is a surname of German origin borne by various notable individuals across fields such as science, sports, and the arts.
-
B.
Fahrenkopf
Fahrenkopf is a surname most prominently associated with Frank J. Fahrenkopf Jr., an American lawyer, lobbyist, and former chairman of the Republican National Committee.
-
C.
Haan
Haan is a town in the German state of North Rhine-Westphalia, known for its location between Düsseldorf and Wuppertal and its mix of residential areas and light industry.
-
D.
Hachen
Hachen is a district (Ortsteil) of the town of Sundern in the Hochsauerland region of North Rhine-Westphalia, Germany.
-
E.
Hartung
Hartung is a German surname borne by various notable individuals across fields such as art, sports, and politics.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3da9c112c81908232dc6908d831cf |
completed | April 18, 2026, 7:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01233cd3d48190b002951881ef670b |
completed | May 11, 2026, 12:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.