Triple
T11370238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chapman University |
E269320
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Orange |
E229476
|
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 | Statement: [Chapman University, city, Orange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orange Context triple: [Chapman University, city, Orange]
-
A.
Orange
Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
-
B.
Orange
Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
-
C.
Orange
chosen
Orange is a small suburban village in Cuyahoga County, Ohio, known for its residential character and proximity to the Cleveland metropolitan area.
-
D.
Orange
Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
-
E.
Orange
Orange is a bright, warm color commonly associated with energy, visibility, and caution, often used in transportation and signage systems.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea8b196881909af9b138661e816d |
completed | April 9, 2026, 6:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5567b67f88190b0412e30b346d36d |
completed | April 19, 2026, 10:26 p.m. |
Created at: April 8, 2026, 9:33 p.m.