Triple
T16609966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Bad Guy |
E403539
|
entity |
| Predicate | chronologyFollowedBy |
P134
|
FINISHED |
| Object | Barcelona |
E9407
|
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: Barcelona | Statement: [Mr. Bad Guy, chronologyFollowedBy, Barcelona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barcelona Context triple: [Mr. Bad Guy, chronologyFollowedBy, Barcelona]
-
A.
Barcelona
Barcelona is a coastal municipality in the province of Sorsogon in the Bicol Region of the Philippines, known for its historic church and scenic seaside views.
-
B.
Barcelona
chosen
Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
-
C.
Barcelonès
Barcelonès is a highly urbanized comarca in Catalonia that includes the city of Barcelona and serves as one of the most densely populated areas in Spain.
-
D.
Madrid
Madrid is the capital and largest city of Spain, renowned for its rich cultural heritage, historic architecture, and vibrant arts and nightlife scenes.
-
E.
Madrid
Madrid is a coastal municipality in the Philippine province of Surigao del Sur on the island of Mindanao.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3609572508190a5d7e6c3e0a8cf95 |
completed | April 18, 2026, 10:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0075aca5c0819092637e0d83ce8ac0 |
completed | May 10, 2026, 12:10 p.m. |
Created at: April 10, 2026, 5:17 a.m.