Triple
T3334342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Dresser |
E70103
|
entity |
| Predicate | screenDebutInMajorRoleFor |
P47973
|
FINISHED |
| Object | Tom Courtenay in this specific part |
—
|
LITERAL 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: Tom Courtenay in this specific part | Statement: [The Dresser, screenDebutInMajorRoleFor, Tom Courtenay in this specific part]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenDebutInMajorRoleFor Context triple: [The Dresser, screenDebutInMajorRoleFor, Tom Courtenay in this specific part]
-
A.
playedRoleIn
Indicates that an entity performed or assumed a specific role or character within a particular event, production, or context.
-
B.
filmDebut
Indicates the first film in which an entity (typically a person) appeared or participated, marking their initial entry into film work.
-
C.
playedBy
Indicates that a role, character, or performance is portrayed or executed by a specific person or agent.
-
D.
playedKeyRoleIn
Indicates that an entity had a major, influential, or decisive impact on the occurrence, outcome, or success of another entity or event.
-
E.
hasMainRole
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
- F. None of above. chosen
Provenance (4 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1961b888190bda38ba301ddc51d |
completed | March 8, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69ada42c2ba8819091136805ce17b39d |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69adaa518ac88190b64f949ace018ab7 |
completed | March 8, 2026, 4:56 p.m. |
Created at: March 8, 2026, 3:12 p.m.