Triple
T3632199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orange City Public Library |
E76981
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Orange City |
E12171
|
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: Orange City | Statement: [Orange City Public Library, city, Orange City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orange City Context triple: [Orange City Public Library, city, Orange City]
-
A.
Orange City
Orange City is the popular nickname of Nagpur, a major city in Maharashtra, India, famed for its extensive orange cultivation and trade.
-
B.
Orange City, Iowa
chosen
Orange City, Iowa is a small northwestern Iowa community known for its Dutch heritage, annual Tulip Festival, and role as the cultural and economic hub of Sioux County.
-
C.
Pasco
Pasco is a city in southeastern Washington State that forms part of the Tri-Cities region along with Kennewick and Richland.
-
D.
Lakeland
Lakeland is a residential neighborhood located within the city of College Park in Prince George's County, Maryland.
-
E.
Ocala
Ocala is a city in north-central Florida known for its thoroughbred horse farms and historic downtown.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc30251d881908284edeb7fe69ad8 |
completed | March 8, 2026, 6:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4882f7f7c8190933b1c358df818ef |
completed | March 13, 2026, 9:57 p.m. |
Created at: March 8, 2026, 3:23 p.m.