Triple
T16647920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ida Lupino |
E404523
|
entity |
| Predicate | actedIn |
P1668
|
FINISHED |
| Object | On Dangerous Ground |
E756968
|
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: On Dangerous Ground | Statement: [Ida Lupino, actedIn, On Dangerous Ground]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: On Dangerous Ground Context triple: [Ida Lupino, actedIn, On Dangerous Ground]
-
A.
On Dangerous Ground
On Dangerous Ground is a fast-paced political thriller novel by Jack Higgins involving high-stakes espionage and international intrigue.
-
B.
On Dangerous Ground
chosen
On Dangerous Ground is a 1952 American film noir crime drama directed by Nicholas Ray, known for its dark psychological themes and stark visual style.
-
C.
On Deadly Ground
On Deadly Ground is a 1994 action film directed by and starring Steven Seagal, centered on an environmentalist oil-rig firefighter battling corporate corruption in Alaska.
-
D.
Edge of Danger
Edge of Danger is a fast-paced thriller novel by Jack Higgins that follows covert operatives entangled in international intrigue and assassination plots.
-
E.
Green for Danger
Green for Danger is a 1946 British mystery film set in a wartime hospital, renowned for its blend of whodunit suspense and dark humor.
- 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37ad66fe88190be582b81719f2ac1 |
completed | April 18, 2026, 12:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084c0e37c819080161d522069aa1d |
completed | May 10, 2026, 1:14 p.m. |
Created at: April 10, 2026, 5:18 a.m.