Triple
T523700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Be |
E10871
|
entity |
| Predicate | includesTrack |
P3284
|
FINISHED |
| Object | Chi-City |
E1659
|
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: Chi-City | Statement: [Be, includesTrack, Chi-City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chi-City Context triple: [Be, includesTrack, Chi-City]
-
A.
Chi-Town
chosen
Chi-Town is a popular nickname for the city of Chicago, reflecting its identity as a major cultural and economic hub in the United States.
-
B.
River City
River City is a popular nickname for Richmond, Virginia, highlighting the city's location along the James River and its historic riverfront character.
-
C.
River City
River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
-
D.
River City
River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
-
E.
Chocolate City
Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
- 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_69a2e84b16c4819088d284c47c3a7968 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1b4f01881908b408357ff113308 |
completed | Feb. 28, 2026, 1:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4c02c26808190b3078fb04f34572c |
completed | March 1, 2026, 10:39 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.