Triple
T7762585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis I of Chalon-Arlay |
E176061
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Orange |
E3952
|
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: [Louis I of Chalon-Arlay, region, Orange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orange Context triple: [Louis I of Chalon-Arlay, region, 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 a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
-
C.
Orange
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.
-
D.
Orange
Orange is one of the color-coded lines of Miami’s Metrorail system, serving as a distinct route that includes Brickell station among its stops.
-
E.
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.
- 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_69c69962923c8190ac74d28b4f9fe0a0 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70404c2108190ad2b900ac9bf582b |
completed | March 27, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7da8b848190b378f694118dfa19 |
completed | March 29, 2026, 6:34 a.m. |
Created at: March 27, 2026, 4:09 p.m.