Triple
T11202757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Husbands |
E265079
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Al Ruban |
E911789
|
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: Al Ruban | Statement: [Husbands, producer, Al Ruban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Al Ruban Context triple: [Husbands, producer, Al Ruban]
-
A.
Al Ruban
chosen
Al Ruban is an American film producer and cinematographer best known for his longtime collaboration with director John Cassavetes on several influential independent films.
-
B.
Rasual
Rasual is the given name of Rasual Butler, an American professional basketball player who competed in the NBA.
-
C.
Rasuil
Rasuil is an alternative name for Raguel, an archangel in various Jewish and Christian traditions often associated with justice, harmony, and fairness.
-
D.
Rabil
Rabil is a small town on the island of Boa Vista in Cape Verde, known for its proximity to beaches, dunes, and the island’s airport.
-
E.
Al Malaki
Al Malaki is a popular nickname for Al Ahli Saudi FC, one of Saudi Arabia’s most prominent and historically successful football clubs.
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8c36c188190bfa4d5f8e6cbbbea |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58ac418f08190b2936e8dbf9fb27d |
completed | April 20, 2026, 2:09 a.m. |
Created at: April 8, 2026, 9:29 p.m.