Triple
T11918742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Henry Bell |
E283596
|
entity |
| Predicate | fieldImpact |
P52396
|
FINISHED |
| Object | development of commercial steam navigation in Europe |
—
|
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: development of commercial steam navigation in Europe | Statement: [Henry Bell, fieldImpact, development of commercial steam navigation in Europe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldImpact Context triple: [Henry Bell, fieldImpact, development of commercial steam navigation in Europe]
-
A.
impactOnField
chosen
Indicates the effect or influence that one entity, action, or development has on a particular field or domain.
-
B.
seasonImpact
Indicates how a particular season influences or affects another entity, condition, or outcome.
-
C.
impactOnStandings
Indicates how an event or outcome affects the relative rankings or standings within a competition or system.
-
D.
encodingImpact
Indicates how one encoding or encoding choice affects, modifies, or constrains another process, representation, or outcome.
-
E.
fielded
Indicates that an entity deployed, presented, or put forward another entity (such as a person, team, or resource) for participation or use in a particular context or activity.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e8dff77481908cacf6ad03df34ac |
completed | April 10, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3632ac8190b13e53c2b5db7125 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:44 p.m.