Triple
T14475787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malika-i-Jahan |
E358966
|
entity |
| Predicate | relatedTitle |
P914
|
FINISHED |
| Object | Malika |
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: Malika | Statement: [Malika-i-Jahan, relatedTitle, Malika]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malika Context triple: [Malika-i-Jahan, relatedTitle, Malika]
-
A.
Malika
chosen
Malika is a feminine given name of Arabic origin commonly used in various Muslim-majority and North African cultures.
-
B.
Malalai
Malalai is an Afghan activist and former politician internationally recognized for her outspoken criticism of warlords, the Taliban, and foreign occupation in Afghanistan.
-
C.
Karima
Karima is a town in northern Sudan known as a gateway to the ancient Nubian archaeological area around Gebel Barkal and the Napatan sites.
-
D.
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.
-
E.
Masmuda
Masmuda were a major Berber tribal confederation of the High Atlas and western Morocco that played a central role in the rise of the Almohad movement.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91fc1fc48190842b09aa03ba79f8 |
completed | April 14, 2026, 7:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd64a0553081909fd88d8f39ed1a01 |
completed | May 8, 2026, 4:20 a.m. |
Created at: April 10, 2026, 1:20 a.m.