Triple
T3758032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Good Vibes |
E82094
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Ms. Teets
Ms. Teets is the central character of the story "Good Vibes," around whom the main events and themes revolve.
|
E385719
|
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: Ms. Teets | Statement: [Good Vibes, hasMainCharacter, Ms. Teets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ms. Teets Context triple: [Good Vibes, hasMainCharacter, Ms. Teets]
-
A.
Ms. Parker
"Ms. Parker" is a track from Chance the Rapper and Jeremih’s collaborative Christmas-themed mixtape "Merry Christmas Lil Mama."
-
B.
Mrs. Wiggins
Mrs. Wiggins is a ditzy, slow-typing secretary character, famously portrayed by Carol Burnett in a recurring comedy sketch on her variety show.
-
C.
Madame Ban
Madame Ban is the honorific title commonly used for Yoo Soon-taek, the wife of former United Nations Secretary-General Ban Ki-moon and a prominent South Korean public figure.
-
D.
Molly Ockett
Molly Ockett was a well-known Abenaki healer and folk figure from the 18th–19th century New England region, remembered for her medical skills, generosity, and close relationships with local settlers.
-
E.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
- 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: Ms. Teets Triple: [Good Vibes, hasMainCharacter, Ms. Teets]
Generated description
Ms. Teets is the central character of the story "Good Vibes," around whom the main events and themes revolve.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ms. Teets Target entity description: Ms. Teets is the central character of the story "Good Vibes," around whom the main events and themes revolve.
-
A.
Ms. Parker
"Ms. Parker" is a track from Chance the Rapper and Jeremih’s collaborative Christmas-themed mixtape "Merry Christmas Lil Mama."
-
B.
Mrs. Wiggins
Mrs. Wiggins is a ditzy, slow-typing secretary character, famously portrayed by Carol Burnett in a recurring comedy sketch on her variety show.
-
C.
Madame Ban
Madame Ban is the honorific title commonly used for Yoo Soon-taek, the wife of former United Nations Secretary-General Ban Ki-moon and a prominent South Korean public figure.
-
D.
Molly Ockett
Molly Ockett was a well-known Abenaki healer and folk figure from the 18th–19th century New England region, remembered for her medical skills, generosity, and close relationships with local settlers.
-
E.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc04d348190b0e4a90d18bdd160 |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e50f77fc8190b7774a7359118c9c |
completed | March 14, 2026, 4:33 a.m. |
| NEDg | Description generation | batch_69b4e5fe22f0819088effd8a0eae72e6 |
completed | March 14, 2026, 4:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4e671e02c819094cae2a3a2abb1b4 |
completed | March 14, 2026, 4:39 a.m. |
Created at: March 8, 2026, 3:35 p.m.