Triple
T10079083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matoury |
E213851
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Cayenne |
E161877
|
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: Cayenne | Statement: [Matoury, borders, Cayenne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cayenne Context triple: [Matoury, borders, Cayenne]
-
A.
Cayenne
chosen
Cayenne is the principal city and administrative center of French Guiana, located on the Atlantic coast in northeastern South America.
-
B.
Naiche
Naiche was the last hereditary chief of the Chiricahua Apache and a prominent leader during the final phase of the Apache resistance against the United States.
-
C.
Darien
Darien is a coastal town in Fairfield County, Connecticut, known for its affluent residential character and location along Long Island Sound.
-
D.
Canela
Canela is a coastal rural municipality in Chile’s Coquimbo Region, known for its small agricultural communities and semi-arid landscapes.
-
E.
Canela
Canela is a popular mountain resort town in southern Brazil known for its cool climate, European-influenced architecture, and nearby natural attractions such as waterfalls and canyons.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd030a0fc819084b523e8e63636fa |
completed | April 2, 2026, 2:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2cbc0388c8190bc10d462068c9e38 |
completed | April 5, 2026, 8:53 p.m. |
Created at: March 30, 2026, 9 p.m.