Triple
T482869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garland |
E9807
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Sachse |
E9809
|
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: Sachse | Statement: [Garland, locatedNear, Sachse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sachse Context triple: [Garland, locatedNear, Sachse]
-
A.
Sachse
chosen
Sachse is a suburban city in the Dallas–Fort Worth metropolitan area of northeastern Texas.
-
B.
Shawnee
The Shawnee are an Algonquian-speaking Native American people historically based in the Ohio Valley, known for their resistance to U.S. expansion in the late 18th and early 19th centuries.
-
C.
Hesse
Hesse is a federal state in central Germany known for its financial hub Frankfurt am Main and its mix of urban centers, forests, and historic towns.
-
D.
Löwenthal
Löwenthal is the maiden surname of Elsa Einstein, who was both the second wife and cousin of physicist Albert Einstein.
-
E.
Starnberg
Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
- 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_69a2e802e2908190ab17c9479e0b6412 |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0b8fe6081909f8ab87bfda6b2d8 |
completed | Feb. 28, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a46c622e648190b6d687fb8efa2123 |
completed | March 1, 2026, 4:42 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.