Triple
T15328732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Layers |
E366478
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Tabu |
E125489
|
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: Tabu | Statement: [Layers, producer, Tabu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabu Context triple: [Layers, producer, Tabu]
-
A.
Tabu
chosen
Tabu is an acclaimed Indian actress known for her powerful performances in both mainstream and art-house cinema, including prominent roles in internationally recognized films.
-
B.
Taboo
Taboo is a writer best known for his work on the project "Ba Bump."
-
C.
Taboo
Taboo is an American rapper, singer, and member of the hip hop group The Black Eyed Peas, known for his energetic performances and distinctive style.
-
D.
Taboo
Taboo is a dark, atmospheric British television drama series set in early 19th-century London, following a mysterious adventurer entangled in political and criminal conspiracies.
-
E.
Taboo
Taboo is a film featuring German actress Franka Potente in a prominent role.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dffd6f88190a0f031ee90c6a7d2 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8af92a88190bd47f1a484f25eb1 |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.