Triple
T82993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madam President |
E1667
|
entity |
| Predicate | appliesWhen |
P1129
|
FINISHED |
| Object | a woman serves as President of the United States |
—
|
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: a woman serves as President of the United States | Statement: [Madam President, appliesWhen, a woman serves as President of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesWhen Context triple: [Madam President, appliesWhen, a woman serves as President of the United States]
-
A.
appliesAt
Indicates that an action, rule, or condition is relevant to or in effect at a specific location, context, or point in time.
-
B.
appliesTo
chosen
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
C.
appliesAcross
Indicates that a condition, rule, or property holds uniformly over multiple items, cases, or contexts.
-
D.
appliedBecauseOf
Indicates that one entity is applied, used, or enacted as a consequence or result of another specified cause or condition.
-
E.
hasCondition
Indicates that an entity possesses, experiences, or is affected by a particular condition or state.
- 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_69a24c8150408190910a693eb51c1f71 |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a25053ca208190a371b0d38000c2b9 |
completed | Feb. 28, 2026, 2:17 a.m. |
| PD | Predicate disambiguation | batch_69a24eb2998c819082681da74601d446 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.