Triple
T12011126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flint v. Stone Tracy Co. |
E285906
|
entity |
| Predicate | relatesToTopic |
P37
|
FINISHED |
| Object | corporate taxation |
—
|
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: corporate taxation | Statement: [Flint v. Stone Tracy Co., relatesToTopic, corporate taxation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatesToTopic Context triple: [Flint v. Stone Tracy Co., relatesToTopic, corporate taxation]
-
A.
relatesToAudience
Indicates a general relationship or relevance between something and a particular audience or group of recipients.
-
B.
moreCloselyRelatedTo
Indicates that one entity has a stronger or closer relationship, connection, or similarity to a second entity than to some other reference entity.
-
C.
relatedTo
chosen
Indicates a general, non-specific relationship or association exists between two entities.
-
D.
frequentlyDiscussedIn
Indicates that a topic, subject, or entity is often the focus of conversation, debate, or mention within a particular context or medium.
-
E.
relatesToProgram
Indicates that one entity has a relevant connection, association, or involvement with a particular program.
- 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903d7777481908cd5a001f75e2ee3 |
completed | April 10, 2026, 2:06 p.m. |
| PD | Predicate disambiguation | batch_69d902b245cc8190af96a9c2bd9c6250 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.