Triple
T37489500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diemelsee |
E931634
|
entity |
| Predicate | hasNearbySettlementOnShore |
P193650
|
FINISHED |
| Object |
Heringhausen
Heringhausen is a lakeside village in the municipality of Diemelsee in Hesse, Germany, known for its scenic setting and outdoor recreation opportunities.
|
E2278935
|
NE FINISHED |
How this triple was built (3 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: Heringhausen | Statement: [Diemelsee, hasNearbySettlementOnShore, Heringhausen]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Heringhausen Triple: [Diemelsee, hasNearbySettlementOnShore, Heringhausen]
Generated description
Heringhausen is a lakeside village in the municipality of Diemelsee in Hesse, Germany, known for its scenic setting and outdoor recreation opportunities.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbySettlementOnShore Context triple: [Diemelsee, hasNearbySettlementOnShore, Heringhausen]
-
A.
hasNearbyCoastalSettlement
chosen
Indicates that one entity has a coastal settlement located in close geographic proximity to it.
-
B.
hasShoreNear
Indicates that one entity is located close enough to another entity’s shore or coastline to be considered nearby.
-
C.
hasCityOnShore
Indicates that a city is located on or directly adjacent to the shore of a body of water.
-
D.
hasNearbyCoast
Indicates that one location is situated close to a coastline or seashore.
-
E.
hasNearbyHarbor
Indicates that one location has a harbor situated close to it in geographic proximity.
- F. None of above.
Provenance (6 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_69f76ec457a4819094eeb3aed9baac11 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a00a2f0d1588190a936ea7df0ef0464 |
completed | May 10, 2026, 3:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41f42211f481909ee1dc3706781a9d |
completed | June 29, 2026, 4:27 a.m. |
| NEDg | Description generation | batch_6a41f4fbde8c819096617301e3ece13c |
completed | June 29, 2026, 4:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41f8d699bc8190a33eea34eff1e4d8 |
completed | June 29, 2026, 4:47 a.m. |
| PD | Predicate disambiguation | batch_6a00a28ccd94819085b5e123f5a4769e |
completed | May 10, 2026, 3:21 p.m. |
Created at: May 3, 2026, 4:17 p.m.