Triple
T10146734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syracuse Orange |
E231721
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Orange |
E316023
|
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: [Syracuse Orange, nickname, Orange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orange Context triple: [Syracuse Orange, nickname, Orange]
-
A.
Orange
Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
-
B.
Orange
chosen
Orange is the nickname and primary identity of Syracuse University's athletic teams, especially its prominent men's basketball program.
-
C.
Orange
Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
-
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 common English surname of likely Norman or French origin, shared by various individuals including the British singer Jason Orange.
- 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_69ca848364f881908a24366a6feec1db |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdebffb7688190b5def02f72154b14 |
completed | April 2, 2026, 4:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e62c1984819095fcb239f11731b4 |
completed | April 5, 2026, 10:46 p.m. |
Created at: March 30, 2026, 9:07 p.m.