Triple
T31754997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vlodrop |
E810532
|
entity |
| Predicate | hasNearbyGermanLocality |
P197400
|
FINISHED |
| Object | Effeld |
—
|
NE NERFINISHED |
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: Effeld | Statement: [Vlodrop, hasNearbyGermanLocality, Effeld]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyGermanLocality Context triple: [Vlodrop, hasNearbyGermanLocality, Effeld]
-
A.
nearbySettlementGR
chosen
Indicates that one settlement is geographically located close to another settlement.
-
B.
hasNearbyTown
Indicates that one location has a town situated close to it in geographic proximity.
-
C.
hasNearbyGeographicalArea
Indicates that one geographical area is located in close spatial proximity to another geographical area.
-
D.
federalStateCapitalNearby
Indicates that the capital city of a federal state is geographically close to a specified reference location or entity.
-
E.
hasMunicipalitySeatNearby
Indicates that the municipality’s administrative seat is located in close proximity to the referenced place or entity.
- F. None of above.
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_69f348e340d48190b780fae618c51464 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69ff136ed2a881908f713401083970d1 |
completed | May 9, 2026, 10:58 a.m. |
| PD | Predicate disambiguation | batch_69ff10f9e3448190b6cb6ea5a67713c1 |
completed | May 9, 2026, 10:48 a.m. |
Created at: April 30, 2026, 11:29 p.m.