Triple
T13707378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 31st parallel north |
E328676
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Jordan |
E11658
|
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: Jordan | Statement: [31st parallel north, passesThrough, Jordan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jordan Context triple: [31st parallel north, passesThrough, Jordan]
-
A.
Jordan
chosen
Jordan is a Middle Eastern country located at the crossroads of Asia, Africa, and Europe, known for its ancient archaeological sites like Petra and its strategic political role in the region.
-
B.
Jordan
Jordan is a municipality in the Philippines that serves as the capital of the island province of Guimaras in the Western Visayas region.
-
C.
Jordan
Jordan is a common given name used by people of all genders in many English-speaking and other countries.
-
D.
Jordan
Jordan was a Formula One racing team and constructor known for launching the careers of several top drivers and competing in the sport during the 1990s and early 2000s.
-
E.
Jordan
Jordan is a scientist known for formally describing the bacterial genus Bradyrhizobium.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dcad18d48c8190a26be865c31975d6 |
completed | April 13, 2026, 8:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f794559e9c81909ef8a6d9b9f480b3 |
completed | May 3, 2026, 6:30 p.m. |
Created at: April 9, 2026, 9:54 p.m.