Triple
T23013216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CART |
E572961
|
entity |
| Predicate | chassisSuppliers |
P25550
|
FINISHED |
| Object |
Lola
Lola is a renowned British race car constructor and engineering company known for designing and building competitive chassis for various international motorsport series.
|
E1566968
|
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: [CART, chassisSuppliers, Lola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lola Context triple: [CART, chassisSuppliers, 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: [CART, chassisSuppliers, Lola]
Generated description
Lola is a renowned British race car constructor and engineering company known for designing and building competitive chassis for various international motorsport series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lola Target entity description: Lola is a renowned British race car constructor and engineering company known for designing and building competitive chassis for various international motorsport series.
-
A.
Lola
Lola is a character portrayed by actress and filmmaker Alice Englert.
-
B.
Lola
Lola is a feminine given name commonly used in various cultures, often as a diminutive of Dolores or a standalone name.
-
C.
Lola
Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
-
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 nickname of Ivo Lola Ribar, a prominent Yugoslav communist leader and World War II partisan hero.
- 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e300008190bb12c6388a8b3280 |
completed | April 29, 2026, 4:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bf2a65a10819087b5954853a95c65 |
completed | May 19, 2026, 5:18 a.m. |
| NEDg | Description generation | batch_6a0bfbc5b19c8190aed3f1199410b996 |
completed | May 19, 2026, 5:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bfccedf2c8190833df43ea2c294ca |
completed | May 19, 2026, 6:01 a.m. |
Created at: April 17, 2026, 3:51 p.m.