Triple
T2804036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wicker Park |
E54009
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Lisa
Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension revolve.
|
E299550
|
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: Lisa | Statement: [Wicker Park, mainCharacter, Lisa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisa Context triple: [Wicker Park, mainCharacter, Lisa]
-
A.
Lisa
Lisa is a central character in the science fiction adventure film "Zathura: A Space Adventure," where she becomes unwittingly involved in her younger brothers' perilous journey through outer space.
-
B.
Lisa
Lisa is the given name of Australian musician and composer Lisa Gerrard, renowned for her work as part of Dead Can Dance and for her film scores.
-
C.
Laura
Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
-
D.
Laura
Laura is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
E.
Jennifer
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
- 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: Lisa Triple: [Wicker Park, mainCharacter, Lisa]
Generated description
Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension revolve.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lisa Target entity description: Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension revolve.
-
A.
Lisa
Lisa is a central character in the science fiction adventure film "Zathura: A Space Adventure," where she becomes unwittingly involved in her younger brothers' perilous journey through outer space.
-
B.
Lisa
Lisa is the given name of Australian musician and composer Lisa Gerrard, renowned for her work as part of Dead Can Dance and for her film scores.
-
C.
Laura
Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
-
D.
Laura
Laura is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
E.
Jennifer
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
- 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_69ab49dcee188190b5c6eca9ae9e3469 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde1409148190a06a401185a26b64 |
completed | March 7, 2026, 8:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc674217c81908177b088cc824e7b |
completed | March 10, 2026, 7:21 a.m. |
| NEDg | Description generation | batch_69afc863133c8190b129a55f8d28966f |
completed | March 10, 2026, 7:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc8d43e608190b33159a464dc2e8c |
completed | March 10, 2026, 7:31 a.m. |
Created at: March 6, 2026, 9:59 p.m.