Triple
T31923605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Carolina Shipbuilding Company |
E815038
|
entity |
| Predicate | hadNumberOfWays |
P172734
|
FINISHED |
| Object | 13 shipways |
—
|
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: 13 shipways | Statement: [North Carolina Shipbuilding Company, hadNumberOfWays, 13 shipways]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadNumberOfWays Context triple: [North Carolina Shipbuilding Company, hadNumberOfWays, 13 shipways]
-
A.
hasNumberOfConvergingAvenues
Indicates the number of distinct avenues that meet or converge at a particular location or junction.
-
B.
hadRoute
Indicates that an entity (such as a service, vehicle, or system) followed, operated on, or was associated with a specific route.
-
C.
hasNumberOfDirections
Indicates that an entity is associated with a specific count of possible directions or orientations.
-
D.
numberOfRoutes
Indicates the total count of distinct routes or paths associated with a given entity or between specified entities.
-
E.
hasNumberOfTrails
Indicates the specific count of trails associated with or available in a given entity.
- 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_69f348f1df848190851bbfb988da3414 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b1fa05a081909ba84d0efc314ec4 |
completed | May 3, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69f6aca7081881909e96a8b05ec086bb |
completed | May 3, 2026, 2:02 a.m. |
| PDg | Predicate description generation | batch_69f6af7d92008190aead47eaae8cc091 |
completed | May 3, 2026, 2:14 a.m. |
Created at: May 1, 2026, 12:03 a.m.