Triple

T14056500

Position	Surface form	Disambiguated ID	Type / Status
Subject	Copenhagen Airport	`E338232`	entity
Predicate	hasTerminal	`P182`	FINISHED
Object	Terminal 2 Terminal 2 is one of the main passenger terminals at Copenhagen Airport, handling a large share of the airport’s international and domestic flights.	`E337214`	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: Terminal 2 | Statement: [Copenhagen Airport, hasTerminal, Terminal 2]

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: Terminal 2
Context triple: [Copenhagen Airport, hasTerminal, Terminal 2]

A. Terminal 2
Terminal 2 is the newer, modern passenger terminal at Mexico City International Airport, serving as a major hub for several domestic and international airlines.
B. Terminal 2
Terminal 2 is one of the main passenger terminals at Nice Côte d’Azur Airport, handling a large share of its domestic and international flights.
C. Terminal 2
Terminal 2 is one of the main passenger terminals at Cairo International Airport, serving a mix of international and regional flights.
D. Terminal 2
Terminal 2 is a major passenger terminal at Ninoy Aquino International Airport in Manila, primarily serving as a hub for Philippine Airlines’ domestic and international flights.
E. Terminal 2
Terminal 2 is a major passenger terminal at Haikou Meilan International Airport in Hainan, China, serving as a key hub for domestic and international flights.
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: Terminal 2
Triple: [Copenhagen Airport, hasTerminal, Terminal 2]

Generated description

Terminal 2 is one of the main passenger terminals at Copenhagen Airport, handling a large share of the airport’s international and domestic flights.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: Terminal 2
Target entity description: Terminal 2 is one of the main passenger terminals at Copenhagen Airport, handling a large share of the airport’s international and domestic flights.

A. Terminal 2 chosen
Terminal 2 is one of the main passenger terminals at Copenhagen Airport Kastrup, handling a large share of the airport’s international and domestic flights.
B. Terminal 2
Terminal 2 is one of the main passenger terminals at Manchester Airport, handling a large share of the airport’s international and domestic flights.
C. Terminal 2
Terminal 2 is one of the main passenger terminals at Mehrabad International Airport in Tehran, handling a significant share of the airport’s domestic flight operations.
D. Terminal 2
Terminal 2 is one of the main passenger terminals at Cancún International Airport, serving a large share of its domestic and international flights.
E. Terminal 2
Terminal 2 is one of the main passenger terminals at Zurich Airport, handling a significant share of the airport’s check-in, security, and boarding operations.
F. None of above.

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_69d81c67ba6c819091935650dfb3b895`	completed	April 9, 2026, 9:38 p.m.
NER	Named-entity recognition	`batch_69de3c8d1aa48190a5055d15d9fa220c`	completed	April 14, 2026, 1:09 p.m.
NED1	Entity disambiguation (via context triple)	`batch_69fcb6608cf8819087ed5d890b82650a`	completed	May 7, 2026, 3:57 p.m.
NEDg	Description generation	`batch_69fcc91405888190ba808b2051f57f19`	completed	May 7, 2026, 5:17 p.m.
NED2	Entity disambiguation (via description)	`batch_69fcc9be3f448190b2edd96c4159f923`	completed	May 7, 2026, 5:19 p.m.

Created at: April 9, 2026, 10:20 p.m.