Triple
T6822387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swansea, Massachusetts Bay (historical) |
E156929
|
entity |
| Predicate | attackedInYear |
P35766
|
FINISHED |
| Object | 1675 |
—
|
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: 1675 | Statement: [Swansea, Massachusetts Bay (historical), attackedInYear, 1675]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: attackedInYear Context triple: [Swansea, Massachusetts Bay (historical), attackedInYear, 1675]
-
A.
wasAttackedInYear
chosen
Indicates that an entity was the target of an attack that occurred in the specified year.
-
B.
attackedIn
Indicates that one entity carried out an attack in the location, context, or time frame specified by another entity or value.
-
C.
countryAttacked
Indicates that one country has carried out an attack against another country.
-
D.
attackedFrom
Indicates that one entity initiated an attack against another entity originating from a specific source location or position.
-
E.
conflictBeganInYear
Indicates that the specified conflict started or first broke out in the given calendar year.
- 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_69c688298a288190af3f285d57f76bbe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d57f69cc8190bbd08a8d447e616f |
completed | March 27, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69c6d09bb4f881909bf20c188cb3e8e1 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:17 p.m.