Triple
T13388708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eissporthalle Langenhagen |
E319511
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Hanover |
E21642
|
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: Hanover | Statement: [Eissporthalle Langenhagen, locatedNear, Hanover]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanover Context triple: [Eissporthalle Langenhagen, locatedNear, Hanover]
-
A.
Hanover
chosen
Hanover is a historic city in northern Germany that served as the capital of the former Kingdom of Hanover and the ancestral seat of the British House of Hanover.
-
B.
Hanover
Hanover is a small town in South Africa, known as the birthplace of prominent trade unionist Zwelinzima Vavi.
-
C.
Hanover
Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
-
D.
Hanover
Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
-
E.
Hanover
Hanover is a surname most notably associated with Donna Hanover, an American journalist, actress, and former First Lady of New York City.
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dba0d3a40081909ba49556130ad0e7 |
completed | April 12, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f72691c8d08190b971d7e914863cc1 |
completed | May 3, 2026, 10:42 a.m. |
Created at: April 9, 2026, 9:34 p.m.