Triple
T5377718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Main |
E113003
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Hanau |
E289017
|
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: Hanau | Statement: [Main, flowsThrough, Hanau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanau Context triple: [Main, flowsThrough, Hanau]
-
A.
Hanau
chosen
Hanau is a town in the German state of Hesse, known as an important regional center and the birthplace of the Brothers Grimm.
-
B.
Hanworth
Hanworth is a suburban residential area in west London, England, known for its parks, local amenities, and proximity to Heathrow Airport.
-
C.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
D.
Weikersheim
Weikersheim is a small historic town in the Tauber Valley of Baden-Württemberg, Germany, known for its Renaissance castle and well-preserved old town.
-
E.
Gehrden
Gehrden is a small town in Lower Saxony, Germany, located near Hanover and known for its surrounding rural villages and scenic landscapes.
- 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_69bd4436a1988190af18dcff7fd306b4 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd86cb13ac81909dc364e7d3605844 |
completed | March 20, 2026, 5:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfea92b36081909aa6e33b8b572f90 |
completed | March 22, 2026, 1:11 p.m. |
Created at: March 20, 2026, 2:03 p.m.