Triple
T2005707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malik |
E43578
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object | Malka |
E227152
|
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: Malka | Statement: [Malik, hasCognate, Malka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malka Context triple: [Malik, hasCognate, Malka]
-
A.
Malika
chosen
Malika is a feminine given name of Arabic origin commonly used in various Muslim-majority and North African cultures.
-
B.
Mella
Mella is a Spanish-language surname most notably associated with Cuban revolutionary leader Julio Antonio Mella.
-
C.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
D.
Malyk
Malyk is a given name, typically a variant spelling of Malik, used as a masculine personal name in various cultures.
-
E.
Micali
Micali is an Italian surname most notably associated with Silvio Micali, a Turing Award–winning computer scientist and cryptographer.
- 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_69a88715dbbc8190b2299e29e955d997 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb898795481909920c1a4c4d62c2d |
completed | March 7, 2026, 5:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1fe068ac8190b0999e4f881d134a |
completed | March 9, 2026, 1:18 a.m. |
Created at: March 4, 2026, 7:37 p.m.