Triple
T9683492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Murray Grieve |
E234345
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Penny Wheep |
E159863
|
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: Penny Wheep | Statement: [Christopher Murray Grieve, notableWork, Penny Wheep]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penny Wheep Context triple: [Christopher Murray Grieve, notableWork, Penny Wheep]
-
A.
Penny Wheep
chosen
Penny Wheep is a poetry collection by Scottish modernist writer Hugh MacDiarmid that reflects his innovative use of Scots language and exploration of national and social themes.
-
B.
Pookie
Pookie is a tragic, crack-addicted informant character from the 1991 crime film "New Jack City," portrayed by Chris Rock.
-
C.
Pinky
Pinky is a 1949 American drama film directed by Elia Kazan that explores race, identity, and passing in the segregated American South.
-
D.
Pippy
Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
-
E.
Winky
Winky is a house-elf from the Harry Potter series, known for her loyalty, tragic fall from grace, and eventual employment at Hogwarts.
- 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_69ca84c99e34819092e5563a7106cfca |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9ccf21a08190a1302b933b9e50be |
completed | April 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1910192e88190b10409ae62c1c948 |
completed | April 4, 2026, 10:30 p.m. |
Created at: March 30, 2026, 8:16 p.m.