Triple
T17159359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tydings–McDuffie Act |
E416434
|
entity |
| Predicate | conditionRelatesTo |
P126339
|
FINISHED |
| Object | foreign relations of the Philippines |
—
|
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: foreign relations of the Philippines | Statement: [Tydings–McDuffie Act, conditionRelatesTo, foreign relations of the Philippines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: conditionRelatesTo Context triple: [Tydings–McDuffie Act, conditionRelatesTo, foreign relations of the Philippines]
-
A.
mayRelateTo
Indicates a possible, but not certain, relationship or association between two entities.
-
B.
subjectRelation
Indicates that one entity stands in a specified relational role or connection to another entity.
-
C.
valueRelation
Indicates a comparative or associative relationship between the values or magnitudes of two or more entities.
-
D.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
E.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
- 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_69d886d279c081909f8ff1f743ddeb69 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f9103e3881908e76cea1c4880779 |
completed | April 18, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69e3830d2a90819092386717dc56f0e8 |
completed | April 18, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69e3873f62108190966c4e741ebd548d |
completed | April 18, 2026, 1:29 p.m. |
Created at: April 10, 2026, 5:37 a.m.