Triple
T12039076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MAPFRE Stadium |
E286614
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Columbus |
E933042
|
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: Columbus | Statement: [MAPFRE Stadium, city, Columbus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Columbus Context triple: [MAPFRE Stadium, city, Columbus]
-
A.
Columbus
Columbus is a major city in western Georgia located on the Chattahoochee River, known for its military base Fort Moore (formerly Fort Benning) and its role as a regional economic and cultural center.
-
B.
Columbus
Columbus is a city in eastern Mississippi known for its historic architecture, role in the American Civil War, and as part of the Golden Triangle region.
-
C.
Columbus
Columbus is the cautious, rule-obsessed protagonist and narrator of the post-apocalyptic comedy film "Zombieland."
-
D.
Columbus
Columbus is the European Space Agency’s research laboratory module attached to the International Space Station, used for a wide range of scientific experiments in microgravity.
-
E.
Columbus
chosen
Columbus is the capital and largest city of Ohio, known for its diverse economy, major universities, and vibrant arts and sports scenes.
- 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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9040a8be881908f4841145a7b4e86 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6e2548a848190a3a72415a5e4d0fd |
completed | May 3, 2026, 5:51 a.m. |
Created at: April 8, 2026, 9:47 p.m.