Triple
T2236260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katia Mann |
E49287
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Mann |
E42631
|
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: Mann | Statement: [Katia Mann, familyName, Mann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mann Context triple: [Katia Mann, familyName, Mann]
-
A.
Mann
chosen
Mann is a common German surname borne by numerous notable figures in literature, politics, and the arts.
-
B.
Mies
Mies is a small Swiss municipality in the canton of Vaud, located along Lake Geneva near the city of Geneva.
-
C.
MAN
MAN is the three-letter IATA airport code for Manchester Airport, a major international airport serving the Greater Manchester area in England.
-
D.
MAN
MAN is a German commercial vehicle and engineering company best known for manufacturing trucks, buses, and diesel engines.
-
E.
Ean
Ean is a given name, typically considered a variant of Ian or John, used primarily in English-speaking contexts.
- 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc093ba0c819091df09a0e018fce1 |
completed | March 7, 2026, 6:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af176447d881909173c62cc8c394a0 |
completed | March 9, 2026, 6:54 p.m. |
Created at: March 4, 2026, 7:47 p.m.