Triple
T36527776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Hamburg Act |
E900352
|
entity |
| Predicate | effectOnHamburg |
P58916
|
FINISHED |
| Object | enlarged territory |
—
|
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: enlarged territory | Statement: [Greater Hamburg Act, effectOnHamburg, enlarged territory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnHamburg Context triple: [Greater Hamburg Act, effectOnHamburg, enlarged territory]
-
A.
foodEffect
Indicates how consuming a particular food influences or changes another entity, such as an organism, condition, or process.
-
B.
effectOnCheese
Indicates the influence or impact that one entity or action has on the state, quality, or characteristics of cheese.
-
C.
effectOnUsage
Indicates how one factor or condition changes the way something is used, including the extent, manner, or frequency of its usage.
-
D.
effectOnDrinkers
Indicates the impact or consequences that something has on individuals who consume alcoholic beverages.
-
E.
effectOnOthers
chosen
Indicates the impact or influence that one entity’s actions, presence, or state has on other entities.
- 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_69f76e5eedb88190a393b8c623f71dd7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd9ff026a48190bfec33deeb3b2c43 |
completed | May 8, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69fd97d805bc8190ba12f429d3ad04c7 |
completed | May 8, 2026, 7:59 a.m. |
Created at: May 3, 2026, 4:11 p.m.