Triple
T16810421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brooke Hogan |
E408592
|
entity |
| Predicate | associatedAct |
P37
|
FINISHED |
| Object |
Stack$
Stack$ is a musical act connected to American singer and television personality Brooke Hogan.
|
E1234451
|
NE FINISHED |
How this triple was built (4 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: Stack$ | Statement: [Brooke Hogan, associatedAct, Stack$]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stack$ Context triple: [Brooke Hogan, associatedAct, Stack$]
-
A.
Stack
Stack is a surname most notably associated with American actor and television host Robert Stack.
-
B.
Stack
Stack is a cross-platform build tool and package manager for Haskell that simplifies project setup, dependency management, and reproducible builds.
-
C.
Operation Stack
Operation Stack was a traffic management system used in Kent, England, to queue freight traffic on the M20 motorway during disruptions to cross-Channel services.
-
D.
java.util.Stack
java.util.Stack is a legacy Java collection class that implements a last-in, first-out (LIFO) stack of objects, extending Vector with methods for pushing, popping, and peeking elements.
-
E.
Stacking
Stacking is a puzzle-adventure video game by Double Fine Productions centered on Russian nesting dolls, where players solve challenges by stacking into different characters to use their unique abilities.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Stack$ Triple: [Brooke Hogan, associatedAct, Stack$]
Generated description
Stack$ is a musical act connected to American singer and television personality Brooke Hogan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stack$ Target entity description: Stack$ is a musical act connected to American singer and television personality Brooke Hogan.
-
A.
Stack
Stack is a surname most notably associated with American actor and television host Robert Stack.
-
B.
Stack
Stack is a cross-platform build tool and package manager for Haskell that simplifies project setup, dependency management, and reproducible builds.
-
C.
Operation Stack
Operation Stack was a traffic management system used in Kent, England, to queue freight traffic on the M20 motorway during disruptions to cross-Channel services.
-
D.
java.util.Stack
java.util.Stack is a legacy Java collection class that implements a last-in, first-out (LIFO) stack of objects, extending Vector with methods for pushing, popping, and peeking elements.
-
E.
Stacking
Stacking is a puzzle-adventure video game by Double Fine Productions centered on Russian nesting dolls, where players solve challenges by stacking into different characters to use their unique abilities.
- F. None of above. chosen
Provenance (5 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2cf680c8190bcd640570c524918 |
completed | April 18, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b290d8e4819082880444b42ffa43 |
completed | May 10, 2026, 4:30 p.m. |
| NEDg | Description generation | batch_6a00b33f21a48190b3178aa1241bb85a |
completed | May 10, 2026, 4:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00b3afb3708190ab7d78561a2e778c |
completed | May 10, 2026, 4:34 p.m. |
Created at: April 10, 2026, 5:23 a.m.