Triple
T2362422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Admissions Clause |
E47303
|
entity |
| Predicate | hasTextBeginning |
P2827
|
FINISHED |
| Object | "New States may be admitted by the Congress into this Union" |
—
|
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: "New States may be admitted by the Congress into this Union" | Statement: [Admissions Clause, hasTextBeginning, "New States may be admitted by the Congress into this Union"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTextBeginning Context triple: [Admissions Clause, hasTextBeginning, "New States may be admitted by the Congress into this Union"]
-
A.
hasTextOpening
chosen
Indicates that an entity begins with or contains a specified initial segment of text.
-
B.
hasText
Indicates that an entity is associated with or contains a specific piece of textual content.
-
C.
hasFirstContinuousTextFrom
Indicates that one entity provides the initial uninterrupted segment of text from which the other entity is derived or begins.
-
D.
beganWith
Indicates that one event, process, or state started with or was initiated by another specified event, process, or state.
-
E.
containsText
Indicates that one entity includes the specified text string within its content.
- 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_69a88a1a4a6081908645b0f2914521ab |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc74501388190adce9b3e51a03ded |
completed | March 7, 2026, 6:35 a.m. |
| PD | Predicate disambiguation | batch_69abc599b92c819093d9e15d4437705d |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:55 p.m.