Triple
T21580627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TCA |
E532511
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | City of Tempe |
—
|
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: City of Tempe | Statement: [TCA, operator, City of Tempe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Tempe Context triple: [TCA, operator, City of Tempe]
-
A.
Tempe
Tempe is a city in the Phoenix metropolitan area of Arizona, known for being home to Arizona State University and its vibrant college-town atmosphere.
-
B.
Tempe
Tempe is a locality within Saint George Parish on the Caribbean island nation of Grenada.
-
C.
Tempe
Tempe is a town in South Sulawesi, Indonesia, serving as the administrative center of Wajo Regency.
-
D.
Tempe Valley
Tempe Valley is a narrow, scenic gorge in the region of Thessaly in central Greece, historically renowned for its natural beauty and mythological associations.
-
E.
Tempe, Arizona, USA
chosen
Tempe, Arizona, USA is a vibrant city in the Phoenix metropolitan area best known as the home of Arizona State University and a major center for education, research, and technology.
- 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_69e0c4618bec8190bcb0feb74568cbb1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeeb5c496c819093113dd5790fca48 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 16, 2026, 6:31 p.m.