Triple
T2632888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jo |
E59674
|
entity |
| Predicate | shortFormOf |
P43
|
FINISHED |
| Object | Jordan |
E242718
|
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: [Jo, shortFormOf, Jordan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jordan Context triple: [Jo, shortFormOf, Jordan]
-
A.
Jordan
Jordan is a municipality in the Philippines that serves as the capital of the island province of Guimaras in the Western Visayas region.
-
B.
Jordan
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.
-
C.
Jordan
Jordan is a popular Nike-owned athletic footwear and apparel brand originally inspired by basketball legend Michael Jordan and known for its iconic Air Jordan sneakers.
-
D.
Jordan
chosen
Jordan is a common given name used by people of all genders in many English-speaking and other countries.
-
E.
Madyan
Madyan is a scenic hill town and tourist resort in Pakistan’s Swat Valley, known for its cool climate, river views, and surrounding mountains.
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8c8d32c819081fc89b91217ed54 |
completed | March 7, 2026, 7:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af90a996188190b3a83a31e69d09ef |
completed | March 10, 2026, 3:31 a.m. |
Created at: March 6, 2026, 9:50 p.m.