Triple
T18243386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Big Sombrero |
E436878
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Tampa |
—
|
NE NERFINISHED |
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: Tampa | Statement: [The Big Sombrero, locatedIn, Tampa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tampa Context triple: [The Big Sombrero, locatedIn, Tampa]
-
A.
Tampa, Florida
chosen
Tampa, Florida is a major city on Florida’s Gulf Coast known for its professional sports teams, port and business center, and role as a key hub in the greater Tampa Bay area.
-
B.
Jacksonville
Jacksonville is a small city in west-central Illinois known for its historic colleges, including Illinois College, and its role as a regional educational and cultural center.
-
C.
Jacksonville
Jacksonville is a small town located in Telfair County in the U.S. state of Georgia.
-
D.
Jacksonville
Jacksonville is a small village located in Athens County in the southeastern region of the U.S. state of Ohio.
-
E.
Orlando
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e4f0548190bc617e6acd17010d |
completed | April 19, 2026, 3:42 p.m. |
Created at: April 10, 2026, 10:33 a.m.