Triple
T1435799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ahlden |
E30556
|
entity |
| Predicate | locatedOn |
P40
|
FINISHED |
| Object | river Aller |
E171453
|
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: river Aller | Statement: [Ahlden, locatedOn, river Aller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: river Aller Context triple: [Ahlden, locatedOn, river Aller]
-
A.
River Aller
chosen
The River Aller is a watercourse in Lower Saxony, Germany, that flows through the town of Ahlden and ultimately feeds into the larger Aller river system.
-
B.
Inn River
The Inn River is a major Alpine river in Central Europe that flows through Switzerland, Austria, and Germany before joining the Danube.
-
C.
Buëch River
The Buëch River is a river in southeastern France that flows through the Alps and Provence regions before joining the Durance.
-
D.
Deûle River
The Deûle River is a canalised river in northern France that flows through the city of Lille and serves as an important waterway in the region.
-
E.
Eberflus
Eberflus is the surname of Matt Eberflus, an American football coach best known as the head coach of the Chicago Bears in the NFL.
- 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_69a498fc69ec8190b61722bd4b67c4d2 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c50250b88190a0fcf3e0cbba0b1a |
completed | March 1, 2026, 11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad232534f48190b1392c2fba119b7c |
completed | March 8, 2026, 7:20 a.m. |
Created at: March 1, 2026, 8 p.m.