Triple
T28889425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lifeboat Carpathia (as depicted in film) |
E732655
|
entity |
| Predicate | visualScale |
P166251
|
FINISHED |
| Object | small open lifeboat |
—
|
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: small open lifeboat | Statement: [Lifeboat Carpathia (as depicted in film), visualScale, small open lifeboat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualScale Context triple: [Lifeboat Carpathia (as depicted in film), visualScale, small open lifeboat]
-
A.
userScale
Indicates that a user adjusts or sets the scale, size, or zoom level of an object, interface, or content.
-
B.
visualDetail
Indicates that one entity provides or specifies the visual characteristics, features, or appearance details of another entity.
-
C.
coversScale
Indicates that one entity spans, includes, or applies across the full range or extent of another entity’s scale.
-
D.
pixelScale
Indicates the ratio or conversion factor between pixel units and real-world or coordinate-space units in a representation or image.
-
E.
areaScale
Indicates a proportional relationship where one area value is a scaled (enlarged or reduced) version of another by a specific factor.
- 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_69f05b07bdec819080cadfe147aa1f25 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f66003a3f48190a2ba6da5aafbb5cb |
completed | May 2, 2026, 8:35 p.m. |
| PD | Predicate disambiguation | batch_69f65c2198208190a3954086c22cfcbf |
completed | May 2, 2026, 8:18 p.m. |
| PDg | Predicate description generation | batch_69f65f75ac608190a62cd6afce14f68e |
completed | May 2, 2026, 8:32 p.m. |
Created at: April 28, 2026, 7:53 a.m.