Triple
T8925929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ur-Nammu |
E212538
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Ur |
E37109
|
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: Ur | Statement: [Ur-Nammu, capital, Ur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ur Context triple: [Ur-Nammu, capital, Ur]
-
A.
Ur
chosen
Ur was one of the most important ancient Sumerian city-states, renowned for its ziggurat and early urban civilization in southern Mesopotamia.
-
B.
UR
UR is the vehicle registration code used on license plates for vehicles registered in the Swiss canton of Uri.
-
C.
UR
UR is the commonly used abbreviation for the University of Redlands, a private liberal arts university in Redlands, California.
-
D.
the U
The U is a common nickname for the University of Utah, a major public research university located in Salt Lake City.
-
E.
UL
UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
- 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_69ca839481d48190b42b037e0d0f636c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66700fb48190874563e535f20437 |
completed | April 1, 2026, 12:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d01732bf408190b64ce7687d91a502 |
completed | April 3, 2026, 7:38 p.m. |
Created at: March 30, 2026, 6:57 p.m.