Triple

T26507328
Position Surface form Disambiguated ID Type / Status
Subject Urumea River E669582 entity
Predicate hasBridge P386 FINISHED
Object Maria Cristina Bridge
Maria Cristina Bridge is an ornate early 20th-century bridge in San Sebastián, Spain, known for its monumental design and role as a key crossing over the Urumea River.
E1731495 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: Maria Cristina Bridge | Statement: [Urumea River, hasBridge, Maria Cristina Bridge]
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: Maria Cristina Bridge
Triple: [Urumea River, hasBridge, Maria Cristina Bridge]
Generated description
Maria Cristina Bridge is an ornate early 20th-century bridge in San Sebastián, Spain, known for its monumental design and role as a key crossing over the Urumea River.

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_69eeb319ec70819090834c2591cf5f1e completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f6138e8f508190898b55c271967d95 completed May 2, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c80bcad48190beb8be951d732f01 completed May 23, 2026, 3:30 p.m.
NEDg Description generation batch_6a11c930ba90819087b58de4a6cf4628 completed May 23, 2026, 3:35 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca61b1408190ab4bda33e53cb27c completed May 23, 2026, 3:40 p.m.
Created at: April 27, 2026, 1:17 a.m.