Triple
T32662112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tucker's Luck |
E835044
|
entity |
| Predicate | characterPeterTuckerJenkinsPortrayedBy |
P1507
|
FINISHED |
| Object | Todd Carty |
—
|
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: Todd Carty | Statement: [Tucker's Luck, characterPeterTuckerJenkinsPortrayedBy, Todd Carty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterPeterTuckerJenkinsPortrayedBy Context triple: [Tucker's Luck, characterPeterTuckerJenkinsPortrayedBy, Todd Carty]
-
A.
characterPlayedBy Jonathan Tucker
Indicates that the specified character is portrayed or acted by Jonathan Tucker.
-
B.
characterPortrayedByBenjaminEvanAinsworth
Indicates that the subject is a character portrayed by Benjamin Evan Ainsworth.
-
C.
portrayedBy
chosen
Indicates that one entity serves as the actor or performer who represents or plays the role of another entity in a work or medium.
-
D.
characterPlayedByDavidTomlinson
Indicates that the character is portrayed or voiced by the actor David Tomlinson.
-
E.
characterPortrayedByFrancisLSullivan
Indicates that a character is portrayed or played by the actor Francis L. Sullivan.
- F. None of above.
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_69f349303ccc8190a70d0f6e8a21d3fb |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff136ed2a881908f713401083970d1 |
completed | May 9, 2026, 10:58 a.m. |
| PD | Predicate disambiguation | batch_69ff10f9e3448190b6cb6ea5a67713c1 |
completed | May 9, 2026, 10:48 a.m. |
Created at: May 1, 2026, 1:08 a.m.