Triple
T37283257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlestown Harbour |
E925457
|
entity |
| Predicate | hasTallShips |
P188855
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Charlestown Harbour, hasTallShips, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTallShips Context triple: [Charlestown Harbour, hasTallShips, yes]
-
A.
numberOfShips
Indicates the quantity of ships associated with a given entity or situation.
-
B.
hasMuseumShip
Indicates that one entity possesses or is associated with a museum ship, typically a preserved vessel displayed for public exhibition.
-
C.
hasNavalComponent
Indicates that something includes, involves, or is associated with a naval or maritime element as part of its composition or structure.
-
D.
hasAircraftCarrier
Indicates that one entity possesses, operates, or includes an aircraft carrier as part of its assets or resources.
-
E.
shipsOfTheLine
Indicates a relationship where the associated entities are classified as ships of the line, i.e., major warships designed to participate in the main line of battle.
- 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_69f76eafe20c8190856d3b996a4c31a7 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
| PDg | Predicate description generation | batch_69fbad1b3ba08190ad69e21461333f2e |
completed | May 6, 2026, 9:05 p.m. |
Created at: May 3, 2026, 4:16 p.m.