Triple
T5132573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruhr area |
E115735
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Herne |
E355366
|
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: Herne | Statement: [Ruhr area, containsCity, Herne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herne Context triple: [Ruhr area, containsCity, Herne]
-
A.
Herne
chosen
Herne is a city in the Ruhr area of North Rhine-Westphalia, Germany, known for its industrial heritage and dense urban character.
-
B.
Herne Hill
Herne Hill is a residential district in South London known for its Victorian architecture, local markets, and proximity to Brockwell Park.
-
C.
Horndean
Horndean is a large village and civil parish in Hampshire, England, situated near the South Downs and functioning mainly as a residential and commuter community.
-
D.
Reydon
Reydon is a village and civil parish in the English county of Suffolk, located near the coastal town of Southwold.
-
E.
Blackheath
Blackheath is a historic village and popular tourist stop in the Blue Mountains of New South Wales, Australia, known for its dramatic cliffs, lookouts, and bushwalking trails.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd784b477c8190926daddb28a255af |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec4c9a14881908a8bf2f73ebf56f7 |
completed | March 21, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:42 p.m.