Triple
T1104087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gulf of Tonkin incident |
E25447
|
entity |
| Predicate | hasNorthVietnameseShipType |
P8971
|
FINISHED |
| Object | torpedo boat |
—
|
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: torpedo boat | Statement: [Gulf of Tonkin incident, hasNorthVietnameseShipType, torpedo boat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNorthVietnameseShipType Context triple: [Gulf of Tonkin incident, hasNorthVietnameseShipType, torpedo boat]
-
A.
hasVesselType
Indicates that an entity is associated with or classified by a specific type of vessel (e.g., ship, boat, or container).
-
B.
shipsWith
Indicates that one entity is delivered, packaged, or provided together with another entity as part of the same shipment or bundle.
-
C.
assistedShip
Indicates that one entity helped or supported a ship in performing an operation, task, or journey.
-
D.
shipTypeInvolved
chosen
Indicates that a particular type or class of ship is involved or participates in a specified event, situation, or relationship.
-
E.
statusOfOtherShips
Indicates the relationship that reports or reflects the current conditions or states (such as position, readiness, or operational status) of other ships.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9e1047481909af1cf8df2a01fff |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b7472c848190b0643872f67084a2 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:43 p.m.