Triple
T1606207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Longshore and Harbor Workers’ Compensation Act |
E34508
|
entity |
| Predicate | appliesToInjuriesOn |
P1129
|
FINISHED |
| Object | navigable waters of the United States |
—
|
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: navigable waters of the United States | Statement: [Longshore and Harbor Workers’ Compensation Act, appliesToInjuriesOn, navigable waters of the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToInjuriesOn Context triple: [Longshore and Harbor Workers’ Compensation Act, appliesToInjuriesOn, navigable waters of the United States]
-
A.
injuriesApprox
Indicates an approximate or estimated number or extent of injuries associated with an event or entity.
-
B.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
C.
injuryType
Indicates the specific kind or category of injury associated with an entity or event.
-
D.
causeOfInjury
Indicates that one entity is the source or reason that another entity sustained an injury.
-
E.
appliesTo
chosen
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
- 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_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93fa6926081908bc78d15c0be3185 |
completed | March 5, 2026, 8:32 a.m. |
| PD | Predicate disambiguation | batch_69a907c35f848190a2428c52e81d013e |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.