Triple
T1369028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Günter Grass |
E30068
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Grass |
E115767
|
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: Grass | Statement: [Günter Grass, familyName, Grass]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grass Context triple: [Günter Grass, familyName, Grass]
-
A.
Grass
Grass is a 1925 silent documentary film that follows the arduous seasonal migration of the Bakhtiari tribe in Iran, co-directed by Merian C. Cooper and Ernest B. Schoedsack.
-
B.
Lucerne
Lucerne is a picturesque Swiss city known for its preserved medieval architecture, lakeside setting on Lake Lucerne, and proximity to the Swiss Alps.
-
C.
Alsike
Alsike is a small locality in Uppsala County, Sweden, known as a growing residential community within Knivsta Municipality.
-
D.
Meadows
chosen
Meadows is a surname most prominently associated with Mark Meadows, a former White House Chief of Staff and U.S. congressman.
-
E.
Plante
Plante is a French-origin surname commonly found in Canada and other Francophone regions, associated with several notable figures in sports, politics, and the arts.
- 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_69acce7ae56c8190970bacb061a71798 |
completed | March 8, 2026, 1:18 a.m. |
Created at: March 1, 2026, 7:57 p.m.