Triple
T703702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harland and Wolff |
E14053
|
entity |
| Predicate | historicalPeakEmployment |
P4936
|
FINISHED |
| Object | tens of thousands of workers |
—
|
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: tens of thousands of workers | Statement: [Harland and Wolff, historicalPeakEmployment, tens of thousands of workers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalPeakEmployment Context triple: [Harland and Wolff, historicalPeakEmployment, tens of thousands of workers]
-
A.
unemploymentPeak
Indicates that the level of unemployment has reached its highest point within a specified time period or context.
-
B.
historicalPeak
chosen
Indicates that the related value or state represents the highest level ever reached by something within a historical or recorded time frame.
-
C.
historicalPeakIndustrialPeriod
Indicates the time period during which an entity reached its highest level of industrial activity or development.
-
D.
populationAtPeak
Indicates that an entity’s population size is measured at its highest recorded level during a specified time or condition.
-
E.
historicalEconomicBase
Indicates the primary type of economic activity or production that historically underpinned or sustained an entity’s economy.
- 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58d4c3c8190ad4527d14bca5e6e |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4edc33881909a978268f6dd5d82 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:36 p.m.