Triple

T25113071
Position Surface form Disambiguated ID Type / Status
Subject Western Panama E629049 entity
Predicate contains P35 FINISHED
Object Gulf of Chiriquí
The Gulf of Chiriquí is a large Pacific coastal gulf in western Panama known for its rich marine biodiversity, mangrove forests, and popular offshore islands and national marine parks.
 E1664047 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: Gulf of Chiriquí | Statement: [Western Panama, contains, Gulf of Chiriquí]
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: Gulf of Chiriquí
Triple: [Western Panama, contains, Gulf of Chiriquí]
Generated description
The Gulf of Chiriquí is a large Pacific coastal gulf in western Panama known for its rich marine biodiversity, mangrove forests, and popular offshore islands and national marine parks.

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_69e2ff3169d08190973b6061d5009abd completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f4657881d88190bce0be95d5e2399c completed May 1, 2026, 8:34 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048f6b5008190bd3d0d9c5ee43cb6 completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a104c201d98819097c0f6f6d356b063 completed May 22, 2026, 12:29 p.m.
NED2 Entity disambiguation (via description) batch_6a104c8588e88190b731de55afbeca52 completed May 22, 2026, 12:31 p.m.
Created at: April 18, 2026, 6:27 a.m.