Triple
T21305652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juma bin Maktoum Al Maktoum |
E525191
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Juma |
—
|
NE NERFINISHED |
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: Juma | Statement: [Juma bin Maktoum Al Maktoum, givenName, Juma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juma Context triple: [Juma bin Maktoum Al Maktoum, givenName, Juma]
-
A.
Juma
chosen
Juma is a Tanzanian politician and diplomat best known for serving as Secretary General of the East African Community.
-
B.
Oshindali
Oshindali is a regional dialect of the Oshiwambo language spoken primarily by communities in northern Namibia and southern Angola.
-
C.
Musa
Musa is a central character in Arundhati Roy’s novel "The Ministry of Utmost Happiness," around whom key political and personal conflicts in Kashmir revolve.
-
D.
Musa
Musa is a central character in the documentary film "The Bengal Tiger at the Baghdad Zoo," which follows the experiences of Iraqis and American soldiers amid the chaos of post-invasion Baghdad.
-
E.
Musa
Musa is a South Korean historical epic film starring Jung Woo-sung, known for its large-scale battle scenes and depiction of warriors during the Ming dynasty era.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b518b8948190ad69cf9a8784d397 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75aa3f90481909b212bdbf00c2bb0 |
completed | April 21, 2026, 11:08 a.m. |
Created at: April 16, 2026, 4:05 p.m.