Triple
T17171202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlie Price |
E416736
|
entity |
| Predicate | businessPartner |
P282
|
FINISHED |
| Object |
Lola
Lola is a glamorous and charismatic drag performer who becomes Charlie Price’s creative partner in revitalizing his struggling shoe factory in the musical "Kinky Boots."
|
E416737
|
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: Lola | Statement: [Charlie Price, businessPartner, Lola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lola Context triple: [Charlie Price, businessPartner, Lola]
-
A.
Lola
Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
-
B.
Lola
Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
-
C.
Lola
"Lola" is a 1970 rock song by The Kinks, famous for its catchy melody and narrative about a romantic encounter that plays with themes of gender identity and ambiguity.
-
D.
Lola
Lola is a lethal, acrobatic henchwoman and primary antagonist in the action film "Transporter 2," known for her distinctive red attire and high-impact fight scenes.
-
E.
Lola
Lola is the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
- 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: Lola Triple: [Charlie Price, businessPartner, Lola]
Generated description
Lola is a glamorous and charismatic drag performer who becomes Charlie Price’s creative partner in revitalizing his struggling shoe factory in the musical "Kinky Boots."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lola Target entity description: Lola is a glamorous and charismatic drag performer who becomes Charlie Price’s creative partner in revitalizing his struggling shoe factory in the musical "Kinky Boots."
-
A.
Lola
chosen
Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
-
B.
Lola
Lola is the seductive, devilish femme fatale character in the musical "Damn Yankees," known for her show-stopping number "Whatever Lola Wants."
-
C.
Lola
Lola is a character featured in the musical "The Origin of Love," likely serving as a significant figure within its narrative about love and identity.
-
D.
Lola
Lola is a character portrayed by actress and filmmaker Alice Englert.
-
E.
Lola
Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
- F. None of above.
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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc097950819095631ee5679e03af |
completed | April 18, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01483f85648190acaeb197013e1f1b |
completed | May 11, 2026, 3:08 a.m. |
| NEDg | Description generation | batch_6a014a1993a48190bf65e590ff57c9c2 |
completed | May 11, 2026, 3:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a014a7fa5208190a0a60649fe6292d1 |
completed | May 11, 2026, 3:18 a.m. |
Created at: April 10, 2026, 5:37 a.m.