Triple
T16622682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Cavanagh |
E403870
|
entity |
| Predicate | televisionSeries |
P3279
|
FINISHED |
| Object | "Trust Me" |
E687740
|
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: "Trust Me" | Statement: [Tom Cavanagh, televisionSeries, "Trust Me"]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: "Trust Me" Context triple: [Tom Cavanagh, televisionSeries, "Trust Me"]
-
A.
Trust Me
Trust Me is a British medical thriller television series that follows a nurse who assumes a doctor’s identity, known in part for starring Jodie Whittaker before her role in Doctor Who.
-
B.
Trust Me
Trust Me is a short story collection by American author John Updike that explores themes of family, faith, and middle-class life in contemporary America.
-
C.
Trust Me
"Trust Me" is a song by the American rock band Culture, recognized as one of their notable works.
-
D.
Trust Me
chosen
Trust Me is an American television drama series that explores the high-pressure world of advertising through the personal and professional struggles of two creative executives.
-
E.
Trust Us with Your Life
Trust Us with Your Life is an improvisational comedy television series in which comedians create scenes based on stories from celebrity guests.
- 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_69d883897eb481909eaaa088ba9918d9 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3754f4f508190a5b4b8511623fcd4 |
completed | April 18, 2026, 12:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084b5afc081908d16f0b43fff20fc |
completed | May 10, 2026, 1:14 p.m. |
Created at: April 10, 2026, 5:17 a.m.