Triple
T18113860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steinberg |
E433549
|
entity |
| Predicate | hasTransport |
P1298
|
FINISHED |
| Object |
Steinberg Station
Steinberg Station is a local railway stop serving the community of Steinberg and connecting it to the surrounding region’s rail network.
|
E1312288
|
NE FINISHED |
How this triple was built (4 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: Steinberg Station | Statement: [Steinberg, hasTransport, Steinberg Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steinberg Station Context triple: [Steinberg, hasTransport, Steinberg Station]
-
A.
Blaustein station
Blaustein station is a local railway stop serving the town of Blaustein in the state of Baden-Württemberg, Germany.
-
B.
Bacharach station
Bacharach station is a small railway stop in the town of Bacharach, Germany, serving regional trains along the scenic Middle Rhine Valley.
-
C.
Tuxedo station
Tuxedo station is a commuter rail stop in Tuxedo, New York, serving passengers on the Port Jervis Line between New York City and Orange County.
-
D.
Vestby Station
Vestby Station is a railway station in Vestby, Norway, serving as a stop on the Østfold Line for regional and commuter trains.
-
E.
Loria station
Loria station is a stop on Buenos Aires’ Line A subway, serving passengers in the Balvanera neighborhood of the city.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Steinberg Station Triple: [Steinberg, hasTransport, Steinberg Station]
Generated description
Steinberg Station is a local railway stop serving the community of Steinberg and connecting it to the surrounding region’s rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steinberg Station Target entity description: Steinberg Station is a local railway stop serving the community of Steinberg and connecting it to the surrounding region’s rail network.
-
A.
Blaustein station
Blaustein station is a local railway stop serving the town of Blaustein in the state of Baden-Württemberg, Germany.
-
B.
Bacharach station
Bacharach station is a small railway stop in the town of Bacharach, Germany, serving regional trains along the scenic Middle Rhine Valley.
-
C.
Tuxedo station
Tuxedo station is a commuter rail stop in Tuxedo, New York, serving passengers on the Port Jervis Line between New York City and Orange County.
-
D.
Vestby Station
Vestby Station is a railway station in Vestby, Norway, serving as a stop on the Østfold Line for regional and commuter trains.
-
E.
Loria station
Loria station is a stop on Buenos Aires’ Line A subway, serving passengers in the Balvanera neighborhood of the city.
- F. None of above. chosen
Provenance (5 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_69d8b90916008190a1f110bd7ced5473 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd4c7888190b85c39decdb0333f |
completed | April 19, 2026, 1:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a039efe4cdc81909f07ed7e74d6233a |
completed | May 12, 2026, 9:43 p.m. |
| NEDg | Description generation | batch_6a039fa823ec8190acb9f399d20800c8 |
completed | May 12, 2026, 9:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03a07a59f88190a7ddd5b612d1575a |
completed | May 12, 2026, 9:49 p.m. |
Created at: April 10, 2026, 10:28 a.m.