Triple
T47361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Lady of the United States |
E929
|
entity |
| Predicate | canBe |
P2463
|
FINISHED |
| Object | female relative of the president |
—
|
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: female relative of the president | Statement: [First Lady of the United States, canBe, female relative of the president]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBe Context triple: [First Lady of the United States, canBe, female relative of the president]
-
A.
canRefer
Indicates that one entity has the ability or permission to mention, point to, or direct attention to another entity.
-
B.
canBeAdaptedBy
Indicates that one entity is capable of being modified, adjusted, or tailored for use by another entity.
-
C.
canHold
Indicates that one entity has the capacity or ability to contain, support, or carry another entity.
-
D.
canBeRemovedBy
Indicates that one entity is capable of eliminating, detaching, or undoing another entity or its effect.
-
E.
availableAs
Indicates that one entity can be used, accessed, or offered in the form, role, or capacity of another entity.
- 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24b1bf2c081908f20e13939b713ff |
completed | Feb. 28, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69a24abd07508190a83ffba5368c1c79 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24b1a42888190b56a5e457e11604f |
completed | Feb. 28, 2026, 1:55 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.