Triple
T25316607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The File on Thelma Jordon |
E634760
|
entity |
| Predicate | hasCourtroomSequence |
P69181
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [The File on Thelma Jordon, hasCourtroomSequence, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCourtroomSequence Context triple: [The File on Thelma Jordon, hasCourtroomSequence, true]
-
A.
hasCourtroomScenes
chosen
Indicates that the work contains one or more scenes set in a courtroom or depicting courtroom proceedings.
-
B.
hasCourtroomsFor
Indicates that an entity provides or contains courtrooms designated for use by another entity or purpose.
-
C.
hasNontraditionalCourtroomSet
Indicates that a legal proceeding takes place in a courtroom environment that departs from standard or traditional courtroom design, layout, or setting.
-
D.
hasCourtInEach
Indicates that an entity possesses or maintains a court in every member of a specified set of locations or jurisdictions.
-
E.
courtroom
Indicates a relationship where a legal proceeding or judicial action takes place within or is associated with a specific courtroom.
- 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_69e75a9847c08190bb02990d06d5ffb7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: April 21, 2026, 1:28 p.m.