Triple
T11084132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detroit Shock |
E262074
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object |
Zap
Zap is the energetic mascot character for the former WNBA team the Detroit Shock, known for entertaining fans at games with lively antics and team spirit.
|
E903943
|
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: Zap | Statement: [Detroit Shock, mascot, Zap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zap Context triple: [Detroit Shock, mascot, Zap]
-
A.
Zapped
Zapped is a Disney Channel original movie starring Zendaya as a tech-savvy teen who gains a smartphone app that lets her control boys’ behavior, leading to comedic chaos and life lessons.
-
B.
Zapped
Zapped is a mystery novel by Carol Higgins Clark featuring her recurring sleuth Regan Reilly in a lighthearted, suspenseful crime caper.
-
C.
ZAP
ZAP is an open-source web application security testing tool developed by OWASP, widely used for finding vulnerabilities in web applications.
-
D.
Zapping
Zapping is a Spanish film that marked the screen debut of actress Paz Vega.
-
E.
Zapped!
Zapped! is a 1982 teen sex comedy film best known for its blend of high school hijinks and science fiction elements, starring Scott Baio as a student who gains telekinetic powers.
- 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: Zap Triple: [Detroit Shock, mascot, Zap]
Generated description
Zap is the energetic mascot character for the former WNBA team the Detroit Shock, known for entertaining fans at games with lively antics and team spirit.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zap Target entity description: Zap is the energetic mascot character for the former WNBA team the Detroit Shock, known for entertaining fans at games with lively antics and team spirit.
-
A.
Zapped
Zapped is a Disney Channel original movie starring Zendaya as a tech-savvy teen who gains a smartphone app that lets her control boys’ behavior, leading to comedic chaos and life lessons.
-
B.
Zapped
Zapped is a mystery novel by Carol Higgins Clark featuring her recurring sleuth Regan Reilly in a lighthearted, suspenseful crime caper.
-
C.
ZAP
ZAP is an open-source web application security testing tool developed by OWASP, widely used for finding vulnerabilities in web applications.
-
D.
Zapping
Zapping is a Spanish film that marked the screen debut of actress Paz Vega.
-
E.
Zapped!
Zapped! is a 1982 teen sex comedy film best known for its blend of high school hijinks and science fiction elements, starring Scott Baio as a student who gains telekinetic powers.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799c0cc3081908448cfb26c08daf5 |
completed | April 9, 2026, 12:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e79854c88190bda69cfbe4ae9d1e |
completed | April 18, 2026, 8:20 p.m. |
| NEDg | Description generation | batch_69e3f2cafc008190a3504999297f1e4e |
completed | April 18, 2026, 9:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3f488819081908f9a4225279cde6b |
completed | April 18, 2026, 9:15 p.m. |
Created at: April 8, 2026, 9:27 p.m.