Triple

T25754592
Position Surface form Disambiguated ID Type / Status
Subject Enrique Malek International Airport E648557 entity
Predicate locatedInCity P40 FINISHED
Object David
David is a major city in western Panama that serves as the capital of Chiriquí Province and an important commercial and transportation hub near the Costa Rican border.
E24770 NE FINISHED

How this triple was built (2 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: David | Statement: [Enrique Malek International Airport, locatedInCity, David]
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: David
Triple: [Enrique Malek International Airport, locatedInCity, David]
Generated description
David is a major city in western Panama that serves as the capital of Chiriquí Province and an important commercial and transportation hub near the Costa Rican border.

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_69e7ab314d788190b3abe19e114080e1 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f5fd81788c819096c045e9ef3ffc08 completed May 2, 2026, 1:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10cbdf98d08190a70a77dbe8ea2430 completed May 22, 2026, 9:34 p.m.
NEDg Description generation batch_6a10cc64dde08190b02c25b583f4c264 completed May 22, 2026, 9:36 p.m.
NED2 Entity disambiguation (via description) batch_6a10ccf464b481909d0b12c1e24c5206 completed May 22, 2026, 9:39 p.m.
Created at: April 22, 2026, 4:38 a.m.