Triple
T841766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cold Spring Harbor, New York |
E18192
|
entity |
| Predicate | hasHistoricalIndustry |
P3008
|
FINISHED |
| Object | whaling |
—
|
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: whaling | Statement: [Cold Spring Harbor, New York, hasHistoricalIndustry, whaling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricalIndustry Context triple: [Cold Spring Harbor, New York, hasHistoricalIndustry, whaling]
-
A.
hasHistoricIndustry
chosen
Indicates that an entity has been associated with a notable or historically significant industry or industrial activity in the past.
-
B.
hasHistoricalEntity
Indicates a relationship where one entity includes, references, or is associated with another entity that existed or is defined in a past historical context.
-
C.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
D.
hasIndustrialHeritage
Indicates that an entity possesses or is associated with historically significant industrial sites, structures, or practices.
-
E.
hasHistoricalContext
Indicates that something is related to, influenced by, or best understood in light of specific past events, conditions, or time periods.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abe6f0dc8190a1bebb5e21f4ceac |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7dfc5c8190890c9df485d73a86 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.