Triple
T9050220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ba Son Shipyard area |
E216862
|
entity |
| Predicate | landmarkType |
P46970
|
FINISHED |
| Object | former industrial waterfront |
—
|
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: former industrial waterfront | Statement: [Ba Son Shipyard area, landmarkType, former industrial waterfront]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landmarkType Context triple: [Ba Son Shipyard area, landmarkType, former industrial waterfront]
-
A.
monumentType
Indicates the specific kind or category of monument that an entity is classified as.
-
B.
isLandmarkFor
Indicates that one entity serves as a notable or significant reference point or attraction for another entity, such as a place, route, or area.
-
C.
notablePlace
Indicates that a place is especially significant, famous, or noteworthy in relation to the subject.
-
D.
hasTouristAttractionRole
Indicates that an entity serves in the capacity or function of a tourist attraction for another entity (such as a place, organization, or area).
-
E.
historicLocationType
chosen
Indicates the specific kind or category of a place based on its historical significance or role.
- 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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b52cc1881909fb011d9a8af2e18 |
completed | April 1, 2026, 12:48 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee566b081909e3cdaf551dbd0ec |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:10 p.m.