Triple
T24045663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gloria Michelle |
E595510
|
entity |
| Predicate | hasNamesakeVessel |
P92488
|
FINISHED |
| Object | NOAA Ship Gloria Michelle |
—
|
NE NERFINISHED |
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: NOAA Ship Gloria Michelle | Statement: [Gloria Michelle, hasNamesakeVessel, NOAA Ship Gloria Michelle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNamesakeVessel Context triple: [Gloria Michelle, hasNamesakeVessel, NOAA Ship Gloria Michelle]
-
A.
namedVesselOf
Indicates that one entity is the specific named vessel (e.g., ship, boat, or craft) associated with or belonging to another entity.
-
B.
vesselNamedAfter
chosen
Indicates that a vessel (such as a ship or boat) bears a name derived from or in honor of a particular person, place, event, or entity.
-
C.
hasVessel
Indicates that one entity possesses, uses, or is associated with a particular vessel (such as a container, ship, or transport medium) in the context of the described relationship or action.
-
D.
shipName
Indicates the name assigned to a specific ship in the relationship.
-
E.
hasHistoricVesselsFrom
Indicates that an entity possesses or includes historic vessels that originate from a specified place or source.
- 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_69e288c06a908190899cad4531f32c9a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d9c9391c819095ea6232fa872d5c |
completed | April 29, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 10:14 p.m.