Triple

T24412475
Position Surface form Disambiguated ID Type / Status
Subject Portuguese Grand Prix E615489 entity
Predicate location P40 FINISHED
Object Circuito da Boavista
Circuito da Boavista is a historic street circuit in Porto, Portugal, known for hosting mid-20th-century Formula One Portuguese Grand Prix races along the city’s waterfront roads.
E1648396 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: Circuito da Boavista | Statement: [Portuguese Grand Prix, location, Circuito da Boavista]
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: Circuito da Boavista
Triple: [Portuguese Grand Prix, location, Circuito da Boavista]
Generated description
Circuito da Boavista is a historic street circuit in Porto, Portugal, known for hosting mid-20th-century Formula One Portuguese Grand Prix races along the city’s waterfront roads.

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_69e2d7e9bfac8190a748952a90957106 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f2958241e48190ae33297c5c5c0e59 completed April 29, 2026, 11:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a100fd9bd3c8190853438f7f509a354 completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136871588190b4e4b4618ab7a400 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10140b2fec8190aa6d805f54926b56 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 2:11 a.m.