Triple
T14779558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magic Carpet |
E347353
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Abu |
E347352
|
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: Abu | Statement: [Magic Carpet, associatedWith, Abu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Abu Context triple: [Magic Carpet, associatedWith, Abu]
-
A.
Abu
chosen
Abu is Aladdin’s mischievous and loyal monkey sidekick in Disney’s animated Aladdin franchise.
-
B.
Abu
Abu is the ancient Egyptian name for Elephantine Island, a historically significant Nile island near Aswan known for its strategic and religious importance.
-
C.
Abu
Abu is an Arabic masculine given name commonly used as a kunya meaning "father of," often appearing as part of traditional Arabic personal names.
-
D.
Abu
Abu is the resourceful young thief and loyal companion to Prince Ahmad in the 1940 fantasy film "The Thief of Bagdad."
-
E.
Abu Lulu
Abu Lulu was a Persian slave and craftsman historically known for assassinating the second Rashidun caliph, Umar ibn al-Khattab, in Medina.
- 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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deca9c7cac8190ba900df95e42e318 |
completed | April 14, 2026, 11:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9683d34c81909ff7486582766620 |
completed | May 9, 2026, 2:05 a.m. |
Created at: April 10, 2026, 1:31 a.m.