Triple
T7853096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leslie Fenton |
E182103
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Poppy |
E432725
|
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: Poppy | Statement: [Leslie Fenton, notableWork, Poppy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Poppy Context triple: [Leslie Fenton, notableWork, Poppy]
-
A.
Poppy
chosen
Poppy is a glamorous love interest in the 1932 gangster film "Scarface," entangled in the dangerous world of crime and ambition surrounding the main character.
-
B.
Poppy
Poppy is the upbeat, pink-haired Troll princess who serves as the optimistic and determined protagonist of the animated film "Trolls."
-
C.
Poppy
Poppy is a feminine given name commonly associated with the bright red flower and often used in English-speaking countries.
-
D.
Poppy Papava
Poppy Papava is a fictional character appearing in the James Bond continuation novel "Devil May Care" by Sebastian Faulks.
-
E.
Snowdrop
Snowdrop is a Mersey Ferry passenger vessel that operates on the River Mersey, carrying commuters and tourists between Liverpool and the Wirral.
- 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_69ca82869ee08190b8f9040dbc2c0467 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb18ed56d481909266d862e0ae152d |
completed | March 31, 2026, 12:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b161a4881909641f3a29c1650b4 |
completed | March 31, 2026, 5:26 a.m. |
Created at: March 30, 2026, 4:51 p.m.