Triple

T25805507

Position	Surface form	Disambiguated ID	Type / Status
Subject	Daegu subway fire	`E649957`	entity
Predicate	vehicleInvolved	`P12443`	FINISHED
Object	Train 1080 Train 1080 was the subway train that became fatally engulfed in flames during the 2003 Daegu subway fire in South Korea, one of the country's deadliest mass-casualty incidents.	`E1695870`	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: Train 1080 | Statement: [Daegu subway fire, vehicleInvolved, Train 1080]

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: Train 1080
Triple: [Daegu subway fire, vehicleInvolved, Train 1080]

Generated description

Train 1080 was the subway train that became fatally engulfed in flames during the 2003 Daegu subway fire in South Korea, one of the country's deadliest mass-casualty incidents.

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

Target predicate: vehicleInvolved
Context triple: [Daegu subway fire, vehicleInvolved, Train 1080]

A. utilityInvolved
Indicates that a utility service or provider is involved in, associated with, or plays a role in the referenced situation or relationship.
B. vehicleBase
Indicates that one entity serves as the foundational or underlying base for a vehicle-related entity or system.
C. vehicleUsed chosen
Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
D. carriagesInvolved
Indicates that specific carriages are participants in, or affected by, the referenced event or situation.
E. depictsVehicle
Indicates that one entity visually represents or portrays a vehicle in an image, artwork, or other depiction.
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_69e7ab35d264819095367f7e80c983ff`	completed	April 21, 2026, 4:52 p.m.
NER	Named-entity recognition	`batch_69f603bb02288190b40cedbed5b9651d`	completed	May 2, 2026, 2:01 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a10da16988081908dd1d0df656610e4`	completed	May 22, 2026, 10:35 p.m.
NEDg	Description generation	`batch_6a10db8c106c8190b80bae3db0d75e67`	completed	May 22, 2026, 10:41 p.m.
NED2	Entity disambiguation (via description)	`batch_6a10dc22616081909237e90fee63a70d`	completed	May 22, 2026, 10:43 p.m.
PD	Predicate disambiguation	`batch_69f602d07590819085ac34b189613104`	completed	May 2, 2026, 1:57 p.m.

Created at: April 22, 2026, 7:02 a.m.