Triple
T38561810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milbertshofen-Am Hart |
E928094
|
entity |
| Predicate | hasUbahnStation |
P28508
|
FINISHED |
| Object |
Milbertshofen station
Milbertshofen station is a Munich U-Bahn rapid transit stop serving the Milbertshofen district in the northern part of the city.
|
E2275185
|
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: Milbertshofen station | Statement: [Milbertshofen-Am Hart, hasUbahnStation, Milbertshofen station]
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: Milbertshofen station Triple: [Milbertshofen-Am Hart, hasUbahnStation, Milbertshofen station]
Generated description
Milbertshofen station is a Munich U-Bahn rapid transit stop serving the Milbertshofen district in the northern part of the city.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUbahnStation Context triple: [Milbertshofen-Am Hart, hasUbahnStation, Milbertshofen station]
-
A.
hasUBahnService
Indicates that a location or facility is served by, or has access to, an underground metro (U-Bahn) transit line.
-
B.
uBahnStationOpened
Indicates that a particular U-Bahn (subway/metro) station has been opened and placed into service.
-
C.
hasRailwayStationOn
Indicates that a railway station is located on or serves a particular railway line, route, or network segment.
-
D.
sBahnStationOpened
Indicates that an S-Bahn station was officially opened or began operation at a certain time.
-
E.
subwayStation
chosen
Indicates that one entity is a subway station associated with, located in, or serving the other entity.
- 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_69f76eb8d1808190a588af29d8b266d6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdaa36f90819093f8661969990c7d |
completed | May 7, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41e04369548190b5115a4868ed1cb6 |
completed | June 29, 2026, 3:02 a.m. |
| NEDg | Description generation | batch_6a41e211cc7c81908b15df4b28d8f2a6 |
completed | June 29, 2026, 3:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41e3b3185c8190849330402cf46bab |
completed | June 29, 2026, 3:17 a.m. |
| PD | Predicate disambiguation | batch_69fcd8fefc588190b063d7ea1ec87b07 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.