Triple
T24188391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Ford Television Theatre |
E599623
|
entity |
| Predicate | hasOpeningSponsorMessage |
P155613
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [The Ford Television Theatre, hasOpeningSponsorMessage, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpeningSponsorMessage Context triple: [The Ford Television Theatre, hasOpeningSponsorMessage, yes]
-
A.
hasSponsor
Indicates that one entity financially or otherwise supports another entity, typically in exchange for recognition or other benefits.
-
B.
hasOpening
Indicates that one entity possesses or features an opening, gap, or entrance that allows access, passage, or exposure.
-
C.
hasOpeningHost
Indicates that an event, show, or program is hosted or introduced by a particular opening host.
-
D.
hasOpeningCompany
Indicates that one entity is the company responsible for opening, initiating, or starting the other entity (such as an account, contract, or process).
-
E.
hasOpeningFeature
Indicates that an entity possesses a specific characteristic, element, or attribute related to its opening or entry point.
- 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_69e288cdc8b88190bf2f835d3cb4ca28 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27c9ddfcc819096697a844b300cce |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c42f942c8190b103ff29a60fef34 |
completed | April 29, 2026, 8:41 a.m. |
| PDg | Predicate description generation | batch_69f27a753ca8819095706970d368f762 |
completed | April 29, 2026, 9:39 p.m. |
Created at: April 17, 2026, 11:35 p.m.