Triple
T7678924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amer River |
E173937
|
entity |
| Predicate | hasNameInDutch |
P13254
|
FINISHED |
| Object | Amer |
E152261
|
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: Amer | Statement: [Amer River, hasNameInDutch, Amer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amer Context triple: [Amer River, hasNameInDutch, Amer]
-
A.
Amer
chosen
Amer is a common Arabic surname borne by various notable individuals across the Middle East and North Africa.
-
B.
Amery
Amery is an English surname most notably associated with a British political family, including Conservative politician Julian Amery.
-
C.
Ameria
Ameria is an ancient Umbrian town in central Italy, known today as Amelia, with significant archaeological and historical remains from pre-Roman and Roman times.
-
D.
D’Amérique
D’Amérique is a modernist artwork by French avant-garde artist Francis Picabia, reflecting his experimental approach within the Dada and early abstract movements.
-
E.
Amerika
Amerika is a novel by Franz Kafka that follows a young European immigrant’s surreal and often absurd experiences in the United States.
- 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_69c6995703e0819081de77361b602e78 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701fe2cc88190b5fd5e1378c32e5b |
completed | March 27, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8a248750481908f0de08aee78c9ba |
completed | March 29, 2026, 3:53 a.m. |
Created at: March 27, 2026, 4:01 p.m.