Triple
T17018148
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lola |
E412873
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Lola-Lola |
E800983
|
NE FINISHED |
How this triple was built (2 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-Lola | Statement: [Lola, hasMainCharacter, Lola-Lola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lola-Lola Context triple: [Lola, hasMainCharacter, Lola-Lola]
-
A.
Lola Lola
chosen
Lola Lola is the seductive cabaret singer portrayed by Marlene Dietrich in the classic 1930 German film "The Blue Angel."
-
B.
LOLA
LOLA is the commonly used abbreviation for "Law & Order: LA," a short-lived spin-off of the long-running "Law & Order" television franchise set in Los Angeles.
-
C.
LOLA
LOLA is a laser altimeter instrument aboard NASA's Lunar Reconnaissance Orbiter used to precisely map the Moon's topography.
-
D.
Land of Lola
"Land of Lola" is a showstopping musical number from the Broadway musical *Kinky Boots*, performed by the character Lola as a bold, glamorous declaration of identity and confidence.
-
E.
Lola in Lola
Lola in "Lola" is the enigmatic cabaret singer and central figure of Jacques Demy’s 1961 French New Wave film, around whom the intertwined romantic lives of several characters revolve.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d480a58c8190a3912d26debb4311 |
completed | April 18, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b4d6cb881909b64b4368fd97fa9 |
completed | May 10, 2026, 11:57 p.m. |
Created at: April 10, 2026, 5:33 a.m.