Triple
T17365820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pod (album) |
E422186
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Auntie
"Auntie" is a track from the Pixies' influential 1988 debut album "Pod."
|
E1265252
|
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: Auntie | Statement: [Pod (album), hasPart, Auntie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Auntie Context triple: [Pod (album), hasPart, Auntie]
-
A.
Aunt T
Aunt T is a kindly, maternal figure from the classic The Twilight Zone episode "The Bewitchin’ Pool," who offers troubled children an escape from their unhappy home lives.
-
B.
Aunt Ju
Aunt Ju is the affectionate nickname for the Junkers Ju 52, a famous German three-engined transport aircraft widely used in the 1930s and during World War II.
-
C.
Aunt Lolly
Aunt Lolly is a candy person from the animated series "Adventure Time," known as one of Princess Bubblegum’s relatives in the Candy Kingdom.
-
D.
Aunt Louie
Aunt Louie is a key character in the crime drama series "Snowfall," known as Franklin Saint’s tough, business-savvy aunt who helps build and manage his growing drug empire.
-
E.
Auntie Fee
Auntie Fee was an American internet personality and home cook who gained viral fame for her humorous, no-frills cooking videos on YouTube and social media.
- 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: Auntie Triple: [Pod (album), hasPart, Auntie]
Generated description
"Auntie" is a track from the Pixies' influential 1988 debut album "Pod."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Auntie Target entity description: "Auntie" is a track from the Pixies' influential 1988 debut album "Pod."
-
A.
Aunt T
Aunt T is a kindly, maternal figure from the classic The Twilight Zone episode "The Bewitchin’ Pool," who offers troubled children an escape from their unhappy home lives.
-
B.
Aunt Ju
Aunt Ju is the affectionate nickname for the Junkers Ju 52, a famous German three-engined transport aircraft widely used in the 1930s and during World War II.
-
C.
Aunt Lolly
Aunt Lolly is a candy person from the animated series "Adventure Time," known as one of Princess Bubblegum’s relatives in the Candy Kingdom.
-
D.
Aunt Louie
Aunt Louie is a key character in the crime drama series "Snowfall," known as Franklin Saint’s tough, business-savvy aunt who helps build and manage his growing drug empire.
-
E.
Auntie Fee
Auntie Fee was an American internet personality and home cook who gained viral fame for her humorous, no-frills cooking videos on YouTube and social media.
- 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a50c9ec8190b518fdf80808af53 |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a019564d2c0819095337b6e769d9ff5 |
completed | May 11, 2026, 8:37 a.m. |
| NEDg | Description generation | batch_6a019b7b645c819087040b18ee447321 |
completed | May 11, 2026, 9:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a019c8392d8819098317ff88a5c6e06 |
completed | May 11, 2026, 9:08 a.m. |
Created at: April 10, 2026, 5:44 a.m.