Triple
T16974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Constitution |
E337
|
entity |
| Predicate | amendmentCount |
P1039
|
FINISHED |
| Object | 27 |
—
|
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: 27 | Statement: [United States Constitution, amendmentCount, 27]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: amendmentCount Context triple: [United States Constitution, amendmentCount, 27]
-
A.
hasElectoralVotes
Indicates that a political entity (such as a state or district) possesses a specified number of votes in an electoral system used to choose an officeholder.
-
B.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
C.
elects
Indicates that one entity selects or chooses another entity for a position, role, or office, typically through a formal voting process.
-
D.
legislature
Indicates that an entity serves as, or is part of, a law-making body or assembly responsible for creating or amending laws for a political unit.
-
E.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a241ea1ea081908e8a81ca97531ba5 |
completed | Feb. 28, 2026, 1:16 a.m. |
| PD | Predicate disambiguation | batch_69a23fec1fe8819080da6f2c745dc8fd |
completed | Feb. 28, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69a241e933288190b02ef5369f7b8834 |
completed | Feb. 28, 2026, 1:16 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.