Triple
T5548574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahmoud |
E145469
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Mehmud |
E439438
|
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: Mehmud | Statement: [Mahmoud, hasVariant, Mehmud]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mehmud Context triple: [Mahmoud, hasVariant, Mehmud]
-
A.
Mahmud
chosen
Mahmud is a masculine given name of Arabic origin commonly used in various Muslim-majority cultures.
-
B.
Ahmad Khan Mahmidzada
Ahmad Khan Mahmidzada is an Afghan actor best known for playing the young Hassan in the film adaptation of "The Kite Runner."
-
C.
Yunus Khan
Yunus Khan was a 15th-century Moghul khan of Moghulistan and the maternal grandfather of the Mughal emperor Babur.
-
D.
Khudayar Khan
Khudayar Khan was a 19th-century ruler of the Kokand Khanate in Central Asia, known for his turbulent reign marked by internal strife and increasing Russian influence in the region.
-
E.
Alamuddin
Alamuddin is the Lebanese Druze family name of prominent human rights lawyer Amal Clooney (née Amal Alamuddin).
- 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_69c008fb879c81909f5bfa56fadc1d46 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01fe143ec8190bb67d2530c92a419 |
completed | March 22, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04cf1c66c819099e2cde5e1c7bec0 |
completed | March 22, 2026, 8:11 p.m. |
Created at: March 22, 2026, 3:35 p.m.