Triple
T20441538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angela Quarles |
E501404
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Earning It
"Earning It" is a romance novel by author Angela Quarles, known for its blend of heartfelt emotion, humor, and engaging character development.
|
E1431729
|
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: Earning It | Statement: [Angela Quarles, notableWork, Earning It]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Earning It Context triple: [Angela Quarles, notableWork, Earning It]
-
A.
Earned It
"Earned It" is a sultry, orchestral R&B ballad by The Weeknd that gained widespread recognition as a hit single from the soundtrack of the film "Fifty Shades of Grey."
-
B.
Earn Enough for Us
"Earn Enough for Us" is a song by the English rock band XTC from their acclaimed 1986 album "Skylarking."
-
C.
Welverdiend
Welverdiend is a small town in South Africa’s Gauteng province, situated within the Merafong City Local Municipality and historically associated with regional mining activities.
-
D.
Get Your Money Up
"Get Your Money Up" is a hip-hop track featured on the album "Undisputed."
-
E.
The Payoff
"The Payoff" is a 1935 American crime drama film starring Lee Tracy as a fast-talking newspaper reporter entangled in corruption and murder.
- 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: Earning It Triple: [Angela Quarles, notableWork, Earning It]
Generated description
"Earning It" is a romance novel by author Angela Quarles, known for its blend of heartfelt emotion, humor, and engaging character development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Earning It Target entity description: "Earning It" is a romance novel by author Angela Quarles, known for its blend of heartfelt emotion, humor, and engaging character development.
-
A.
Earned It
"Earned It" is a sultry, orchestral R&B ballad by The Weeknd that gained widespread recognition as a hit single from the soundtrack of the film "Fifty Shades of Grey."
-
B.
Earn Enough for Us
"Earn Enough for Us" is a song by the English rock band XTC from their acclaimed 1986 album "Skylarking."
-
C.
Welverdiend
Welverdiend is a small town in South Africa’s Gauteng province, situated within the Merafong City Local Municipality and historically associated with regional mining activities.
-
D.
Get Your Money Up
"Get Your Money Up" is a hip-hop track featured on the album "Undisputed."
-
E.
The Payoff
"The Payoff" is a 1935 American crime drama film starring Lee Tracy as a fast-talking newspaper reporter entangled in corruption and murder.
- 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_69e0b4ab3cfc8190ac9bf32e932316b1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e685f2e8888190a2e0d6b2bf6c905d |
completed | April 20, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0883ff8e6c8190b8699ea023e98799 |
completed | May 16, 2026, 2:49 p.m. |
| NEDg | Description generation | batch_6a08860f244c81909f2602704a19b0c6 |
completed | May 16, 2026, 2:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08868eadd48190837d0fbefb3da02a |
completed | May 16, 2026, 3 p.m. |
Created at: April 16, 2026, 11:31 a.m.