Triple
T8602050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mireille Enos |
E203701
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Big Love |
E83219
|
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: Big Love | Statement: [Mireille Enos, notableWork, Big Love]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Big Love Context triple: [Mireille Enos, notableWork, Big Love]
-
A.
Big Love
chosen
Big Love is an HBO television drama series that explores the complex lives of a modern-day polygamist family in suburban Utah.
-
B.
Big Love
"Big Love" is a song by the Black Eyed Peas featured on their concept album *Masters of the Sun Vol. 1*.
-
C.
Mad Love
"Mad Love" is a 1935 psychological horror film starring Peter Lorre (credited as László Löwenstein), known for its macabre tale of obsession and surgical mutilation.
-
D.
Mad Love
"Mad Love" is a popular dancehall-pop song by Jamaican artist Sean Paul, known for its catchy hook and club-friendly production.
-
E.
Love, Inc.
Love, Inc. is an American television sitcom that follows a group of dating consultants as they help clients find love while navigating their own romantic lives.
- 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_69ca832b56948190ba751cec255308f1 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46da609881909a6d851915e8df14 |
completed | March 31, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea8f13f7081908317c1b2d87a51b2 |
completed | April 2, 2026, 5:35 p.m. |
Created at: March 30, 2026, 6:24 p.m.