Triple
T1627877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Be |
E35186
|
entity |
| Predicate | hasTrack |
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, hasTrack, Chi-City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chi-City Context triple: [Be, hasTrack, 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.
Red City
Red City is a popular nickname for Marrakesh, the historic Moroccan metropolis famed for its reddish sandstone buildings and city walls.
-
C.
Silk City
Silk City is a historic nickname for Paterson, New Jersey, reflecting its past prominence as a major center of silk production in the United States.
-
D.
Silk City
Silk City is a popular nickname for Rajshahi, a major city in western Bangladesh historically renowned for its silk industry and fine silk products.
-
E.
River City
River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa622ca9bc8190b99e90295cb01646 |
completed | March 6, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58d3808c819086a4a66e66905e8a |
completed | March 8, 2026, 11:09 a.m. |
Created at: March 4, 2026, 7:28 p.m.