Triple
T1834733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheck Wes |
E41039
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Khadimou |
E149448
|
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: Khadimou | Statement: [Sheck Wes, givenName, Khadimou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Khadimou Context triple: [Sheck Wes, givenName, Khadimou]
-
A.
Belhamed
Belhamed is a locality in Libya that was the site of significant fighting during World War II’s North African campaign.
-
B.
Mohandessin
Mohandessin is a prominent, upscale district in Giza, Egypt, known for its residential neighborhoods, commercial avenues, and vibrant urban life.
-
C.
Hassan
Hassan is a key antagonist in Lord Byron’s narrative poem "The Giaour," depicted as a powerful Ottoman leader whose actions drive the poem’s central conflict.
-
D.
Hamed
chosen
Hamed is a masculine given name commonly used in Arabic-speaking and Muslim-majority cultures.
-
E.
Sahnun
Sahnun was a prominent 9th-century Islamic jurist from North Africa whose compilation of legal opinions, the Mudawwana, became a foundational text of the Maliki school of Sunni jurisprudence.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb026aa7c8190bc988d3ee0fd9f41 |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adc9b4b4f08190a0e5ad50de5c0ba8 |
completed | March 8, 2026, 7:10 p.m. |
Created at: March 4, 2026, 7:33 p.m.