Triple
T12650604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herborn Academy |
E302146
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Herborn |
E302146
|
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: Herborn | Statement: [Herborn Academy, locatedIn, Herborn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herborn Context triple: [Herborn Academy, locatedIn, Herborn]
-
A.
Herborn
chosen
Herborn is a historic town in the German state of Hesse, known for its medieval old town and former academy that was an important center of Reformed Protestant scholarship.
-
B.
Langenhain
Langenhain is a district of the town Hofheim am Taunus in the German state of Hesse, known for its residential character and proximity to the Taunus hills.
-
C.
Wernborn
Wernborn is a village and district of the town of Usingen in the Hochtaunus region of Hesse, Germany.
-
D.
Berlinghausen
Berlinghausen is a village within the municipality of Möhnesee in North Rhine-Westphalia, Germany.
-
E.
Hammelburg
Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9615f28cc81908e37249d7ab5ed74 |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6687dad6c8190bb72bfebf6636a37 |
completed | May 2, 2026, 9:11 p.m. |
Created at: April 9, 2026, 5:18 p.m.