Triple
T35966491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Daniel J. Morrell shipwreck |
E1040155
|
entity |
| Predicate | bowSection |
P184481
|
FINISHED |
| Object | remained afloat briefly |
—
|
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: remained afloat briefly | Statement: [SS Daniel J. Morrell shipwreck, bowSection, remained afloat briefly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bowSection Context triple: [SS Daniel J. Morrell shipwreck, bowSection, remained afloat briefly]
-
A.
bowSectionCondition
Indicates the condition or state of the bow section of an object, typically assessing its integrity, damage, or maintenance status.
-
B.
bowGunner
Indicates a relationship where an entity uses or operates a bow as a ranged weapon, typically in a combat or hunting context.
-
C.
bowDepth
Indicates the degree or extent to which one entity bows in relation to another.
-
D.
hasBow
Indicates that one entity possesses, carries, or is equipped with a bow.
-
E.
hasBowType
Indicates that an entity possesses or is associated with a specific type or category of bow.
- 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_69f76e26b21081909fd9ffb3aff6c77a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:07 p.m.