Triple
T13373175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JoJo |
E319117
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Mad Love.
Mad Love is a work associated with JoJo, likely recognized as one of the artist’s notable creative releases.
|
E1036207
|
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: Mad Love. | Statement: [JoJo, notableWork, Mad Love.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mad Love. Context triple: [JoJo, notableWork, Mad Love.]
-
A.
Mad Love
"Mad Love" is a 1935 psychological horror film starring Peter Lorre (credited as László Löwenstein), known for its macabre tale of obsession and surgical mutilation.
-
B.
Mad Love
"Mad Love" is a popular dancehall-pop song by Jamaican artist Sean Paul, known for its catchy hook and club-friendly production.
-
C.
Mad Love
Mad Love is a 2010s American television series that blends romantic comedy with ensemble relationship drama set in New York City.
-
D.
Mad Love (TV series)
Mad Love is a short-lived American romantic comedy television series that follows the intertwined love lives of four friends in New York City.
-
E.
Mad Love in New York City
Mad Love in New York City is a memoir by Arielle Holmes recounting her experiences of homelessness, addiction, and tumultuous romance on the streets of New York City.
- 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: Mad Love. Triple: [JoJo, notableWork, Mad Love.]
Generated description
Mad Love is a work associated with JoJo, likely recognized as one of the artist’s notable creative releases.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mad Love. Target entity description: Mad Love is a work associated with JoJo, likely recognized as one of the artist’s notable creative releases.
-
A.
Mad Love
"Mad Love" is a 1935 psychological horror film starring Peter Lorre (credited as László Löwenstein), known for its macabre tale of obsession and surgical mutilation.
-
B.
Mad Love
"Mad Love" is a popular dancehall-pop song by Jamaican artist Sean Paul, known for its catchy hook and club-friendly production.
-
C.
Mad Love
Mad Love is a 2010s American television series that blends romantic comedy with ensemble relationship drama set in New York City.
-
D.
Mad Love (TV series)
Mad Love is a short-lived American romantic comedy television series that follows the intertwined love lives of four friends in New York City.
-
E.
Mad Love in New York City
Mad Love in New York City is a memoir by Arielle Holmes recounting her experiences of homelessness, addiction, and tumultuous romance on the streets of New York City.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadcda64a48190b53243a763cd175b |
completed | April 11, 2026, 11:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f72682c7f08190b8553ca22734df27 |
completed | May 3, 2026, 10:42 a.m. |
| NEDg | Description generation | batch_69f7270bf9308190a3e9427ffce0e3ee |
completed | May 3, 2026, 10:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f727d063c4819084b4990a0d759f79 |
completed | May 3, 2026, 10:47 a.m. |
Created at: April 9, 2026, 9:33 p.m.