Triple
T21398788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Criss Cross |
E527856
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Meg Randall |
—
|
NE NERFINISHED |
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: Meg Randall | Statement: [Criss Cross, starring, Meg Randall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meg Randall Context triple: [Criss Cross, starring, Meg Randall]
-
A.
Rachel McCleary
Rachel McCleary is an American economist and scholar known for her work on the intersection of religion, culture, and economic development.
-
B.
Laura Rister
Laura Rister is a film producer and executive known for her work on independent and prestige projects, including the financial thriller "Margin Call."
-
C.
Diane Coulston
Diane Coulston is a teenage schoolgirl in the film "T2 Trainspotting," known for her past relationship with protagonist Mark Renton and her sharp, grounded perspective on the aging former heroin users.
-
D.
Mary Beth Hughes
chosen
Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
-
E.
Melissa Ross
Melissa Ross is a television producer known for her work on the home design and lifestyle program "Ideal Home."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b520ee3c8190abddbee7e37e834c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee62cf3e808190847ad66d2e65f9f2 |
completed | April 26, 2026, 7:09 p.m. |
Created at: April 16, 2026, 5:14 p.m.