Triple
T5579190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stamp Act Congress |
E146598
|
entity |
| Predicate | hasNumberOfColoniesRepresented |
P3403
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Stamp Act Congress, hasNumberOfColoniesRepresented, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfColoniesRepresented Context triple: [Stamp Act Congress, hasNumberOfColoniesRepresented, 9]
-
A.
numberOfColoniesRepresented
chosen
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or context.
-
B.
numberOfColonies
Indicates the count of distinct colonies associated with or possessed by a given entity.
-
C.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
-
D.
colonyOf
Indicates that one entity is a colony belonging to, founded by, or politically dependent on another entity.
-
E.
numberOfCommunitiesRepresented
Indicates the count of distinct communities that are represented in relation to a given entity 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_69c0090287a08190b4098411effe970c |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0206bfea08190aa2c8df9f88013ac |
completed | March 22, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69c01b147cc081909237f3f2967d4cb8 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:37 p.m.