Triple
T8925671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Umma |
E212533
|
entity |
| Predicate | hasDeity |
P5606
|
FINISHED |
| Object | Shara |
E767165
|
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: Shara | Statement: [Umma, hasDeity, Shara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shara Context triple: [Umma, hasDeity, Shara]
-
A.
Shara
chosen
Shara is an ancient Mesopotamian deity, primarily known as the warrior god and tutelary divine figure associated with the city-state of Umma in Sumer.
-
B.
Sharya
Sharya is a town in Kostroma Oblast, Russia, known as a regional railway junction and logging center.
-
C.
Shira
Shira is the eroded western volcanic cone and plateau of Mount Kilimanjaro, forming one of the mountain’s three main summits.
-
D.
Shera
Shera is the anthropomorphic tiger mascot created to represent and promote the 2010 Commonwealth Games held in Delhi, India.
-
E.
Katisha
Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
- 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_69cfc932f9848190a2571cfc28353088 |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:57 p.m.