Triple

T27478594
Position Surface form Disambiguated ID Type / Status
Subject Puerto Villamil E693533 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Wall of Tears
The Wall of Tears is a historic stone wall on Isabela Island in the Galápagos, built by prisoners in the mid-20th century and now visited as a somber reminder of the island’s penal colony past.
 E1774463 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: Wall of Tears | Statement: [Puerto Villamil, hasNearbyAttraction, Wall of Tears]
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: Wall of Tears
Triple: [Puerto Villamil, hasNearbyAttraction, Wall of Tears]
Generated description
The Wall of Tears is a historic stone wall on Isabela Island in the Galápagos, built by prisoners in the mid-20th century and now visited as a somber reminder of the island’s penal colony past.

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_69ef5381f2648190a2392d0fab833095 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62e463b8c8190822be73c3270fe16 completed May 2, 2026, 5:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbe92d90819081aefa21893432bb completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bc906eb481908d12f171b1230dbe completed May 24, 2026, 8:53 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd3f12b481908606b8e373ff408a completed May 24, 2026, 8:56 a.m.
Created at: April 27, 2026, 12:58 p.m.