Triple
T158949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madam Secretary |
E3237
|
entity |
| Predicate | usedWhen |
P4341
|
FINISHED |
| Object | the office of Secretary of the Army is held by a woman |
—
|
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: the office of Secretary of the Army is held by a woman | Statement: [Madam Secretary, usedWhen, the office of Secretary of the Army is held by a woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedWhen Context triple: [Madam Secretary, usedWhen, the office of Secretary of the Army is held by a woman]
-
A.
usedFor
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
-
B.
usedDuring
chosen
Indicates that one entity is employed, applied, or active in the course of another entity’s process, event, or time period.
-
C.
usedWith
Indicates that one entity is typically or appropriately employed together with another entity in a combined or complementary use.
-
D.
usedOn
Indicates that one entity is applied to, operated on, or otherwise utilized in relation to another entity.
-
E.
usedAt
Indicates that something is employed, applied, or utilized at a particular place, time, or context.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a2583169a0819081b658882e5bc452 |
completed | Feb. 28, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69a25660c2a48190b4174d5e6da3cb9d |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.