Triple
T16604353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regional New South Wales |
E403412
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Orange
Orange is a regional city in New South Wales, Australia, known for its cool climate, wineries, and agricultural production.
|
E70926
|
NE FINISHED |
How this triple was built (4 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: [Regional New South Wales, hasPart, Orange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orange Context triple: [Regional New South Wales, hasPart, Orange]
-
A.
Orange
Orange is a small town in north-central Massachusetts known for its rural character, historic mill village roots, and location along the Millers River.
-
B.
Orange
Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
-
C.
Orange
"Orange" is a Pulitzer Prize-winning composition by contemporary American composer Caroline Shaw, known for its inventive blend of classical and modern musical elements.
-
D.
Orange
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
-
E.
Orange
Orange is a small suburban village in Cuyahoga County, Ohio, known for its residential character and proximity to the Cleveland metropolitan area.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Orange Triple: [Regional New South Wales, hasPart, Orange]
Generated description
Orange is a regional city in New South Wales, Australia, known for its cool climate, wineries, and agricultural production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orange Target entity description: Orange is a regional city in New South Wales, Australia, known for its cool climate, wineries, and agricultural production.
-
A.
Orange
chosen
Orange is a regional city in the Central Tablelands of New South Wales, Australia, known for its cool-climate wines, agriculture, and growing tourism industry.
-
B.
Orange
Orange is a city in southern California’s Inland Empire region, known for its historic Old Towne district and well-preserved early-20th-century architecture.
-
C.
Orange
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
-
D.
Orange
Orange is a small town in north-central Massachusetts known for its rural character, historic mill village roots, and location along the Millers River.
-
E.
Orange
Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
- F. None of above.
Provenance (5 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3608ff1a481909084e7ad984b0f95 |
completed | April 18, 2026, 10:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0075a67abc8190b0f29a9e4589befb |
completed | May 10, 2026, 12:10 p.m. |
| NEDg | Description generation | batch_6a0079dd3630819095fcf8044d9335cc |
completed | May 10, 2026, 12:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a007aa7180c8190b79db43e5cac19d3 |
completed | May 10, 2026, 12:31 p.m. |
Created at: April 10, 2026, 5:17 a.m.