Triple

T25836409

Position	Surface form	Disambiguated ID	Type / Status
Subject	Genthin rail disaster of 1939	`E650812`	entity
Predicate	involvedTrain	`P169900`	FINISHED
Object	D 10 express train The D 10 express train was a German long-distance passenger service that became historically notable for its involvement in the 1939 Genthin rail disaster.	`E1698344`	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: D 10 express train | Statement: [Genthin rail disaster of 1939, involvedTrain, D 10 express train]

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: D 10 express train
Triple: [Genthin rail disaster of 1939, involvedTrain, D 10 express train]

Generated description

The D 10 express train was a German long-distance passenger service that became historically notable for its involvement in the 1939 Genthin rail disaster.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: involvedTrain
Context triple: [Genthin rail disaster of 1939, involvedTrain, D 10 express train]

A. numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
B. maintainsTrainsFor
Indicates that one entity is responsible for servicing, repairing, or otherwise keeping trains operational for another entity.
C. trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
D. trainsInDiscipline
Indicates that one entity undergoes training or instruction within a particular discipline, field, or area of expertise associated with another entity.
E. alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training another entity.
F. None of above. chosen

Provenance (7 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_69e7ab37438081908f1ccf6284839520`	completed	April 21, 2026, 4:52 p.m.
NER	Named-entity recognition	`batch_69f688d015908190ad5df37030ecf332`	completed	May 2, 2026, 11:29 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a10da2dc6a881909696e3c5d356bfa8`	completed	May 22, 2026, 10:35 p.m.
NEDg	Description generation	`batch_6a10db5f1f8c8190a2a6de0f0a03bddf`	completed	May 22, 2026, 10:40 p.m.
NED2	Entity disambiguation (via description)	`batch_6a10dc421ba4819091ac155dff903e9d`	completed	May 22, 2026, 10:44 p.m.
PD	Predicate disambiguation	`batch_69f68609c0b08190a8e1238a4d97c270`	completed	May 2, 2026, 11:17 p.m.
PDg	Predicate description generation	`batch_69f688034580819086a0f9100645f8ba`	completed	May 2, 2026, 11:25 p.m.

Created at: April 22, 2026, 7:43 a.m.