Triple
T4973951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Dock |
E111719
|
entity |
| Predicate | waterFilled |
P60730
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [North Dock, waterFilled, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterFilled Context triple: [North Dock, waterFilled, true]
-
A.
waterVolume
Indicates the amount of water present in or associated with an entity, typically measured as a volume.
-
B.
waterColumnZone
Indicates the specific vertical region or layer within a body of water where an entity or process is located or occurs.
-
C.
waterFeature
Indicates the presence of a natural or artificial body or flow of water associated with the subject.
-
D.
beamWaterline
Indicates the width of a vessel measured at the waterline, representing the lateral distance across the hull where it meets the water’s surface.
-
E.
waterBoard
Indicates subjecting someone to a form of torture that simulates drowning by pouring water over a cloth covering their face.
- 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_69bd441a0eb481908050fa4273b19eae |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd730a7590819088ab8d49c5c88c2f |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd7146e6e881908a55ab2756b631f6 |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd73089f548190834103366e24ab40 |
completed | March 20, 2026, 4:17 p.m. |
Created at: March 20, 2026, 1:33 p.m.