Triple
T1369027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Günter Grass |
E30068
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Günter |
E172627
|
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: Günter | Statement: [Günter Grass, givenName, Günter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Günter Context triple: [Günter Grass, givenName, Günter]
-
A.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
-
B.
Hans-Jürgen
Hans-Jürgen is a masculine German given name, typically used as a compound first name combining "Hans" and "Jürgen."
-
C.
Gerhard
Gerhard is a masculine given name of German origin, historically common in German-speaking countries.
-
D.
Günther
chosen
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
E.
Erich
Erich is a masculine given name of German origin, commonly used in German-speaking countries and beyond.
- 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_69a498f912008190a376a98b207b2071 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c2d60fdc8190a9954b74ca2b2541 |
completed | March 1, 2026, 10:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1fae5aa08190b6aa50b543a175b8 |
completed | March 9, 2026, 1:17 a.m. |
Created at: March 1, 2026, 7:57 p.m.