Triple
T9623378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lena Headey |
E232398
|
entity |
| Predicate | role |
P268
|
FINISHED |
| Object |
Ma-Ma
Ma-Ma is the ruthless, scarred gang leader and primary antagonist in the 2012 science fiction action film "Dredd."
|
E810213
|
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: Ma-Ma | Statement: [Lena Headey, role, Ma-Ma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ma-Ma Context triple: [Lena Headey, role, Ma-Ma]
-
A.
La MaMa
La MaMa is a renowned Off-Off-Broadway experimental theater in New York City known for fostering avant-garde performance and emerging artists.
-
B.
Nana Mama
Nana Mama is Alex Cross’s wise, strong-willed grandmother and primary caregiver, known for her moral backbone and nurturing presence throughout the Alex Cross crime novel series.
-
C.
MAMA
MAMA (Murray Art Museum Albury) is a contemporary art museum and cultural venue located in Albury, New South Wales, Australia.
-
D.
MAMA
MAMA is the commonly used acronym for the MTV Africa Music Awards, an annual event celebrating contemporary African music and artists.
-
E.
Maamme
Maamme is the national anthem of Finland, known for its patriotic lyrics and prominent role in Finnish national ceremonies and sporting events.
- 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: Ma-Ma Triple: [Lena Headey, role, Ma-Ma]
Generated description
Ma-Ma is the ruthless, scarred gang leader and primary antagonist in the 2012 science fiction action film "Dredd."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ma-Ma Target entity description: Ma-Ma is the ruthless, scarred gang leader and primary antagonist in the 2012 science fiction action film "Dredd."
-
A.
La MaMa
La MaMa is a renowned Off-Off-Broadway experimental theater in New York City known for fostering avant-garde performance and emerging artists.
-
B.
Nana Mama
Nana Mama is Alex Cross’s wise, strong-willed grandmother and primary caregiver, known for her moral backbone and nurturing presence throughout the Alex Cross crime novel series.
-
C.
MAMA
MAMA (Murray Art Museum Albury) is a contemporary art museum and cultural venue located in Albury, New South Wales, Australia.
-
D.
MAMA
MAMA is the commonly used acronym for the MTV Africa Music Awards, an annual event celebrating contemporary African music and artists.
-
E.
Maamme
Maamme is the national anthem of Finland, known for its patriotic lyrics and prominent role in Finnish national ceremonies and sporting events.
- 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_69ca848793ec8190a93a12383a754dc0 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9ad650a4819096258665bc3f410b |
completed | April 1, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1797ab3d4819088da04fd5d00386b |
completed | April 4, 2026, 8:50 p.m. |
| NEDg | Description generation | batch_69d17a96725c8190aca4a5303952171b |
completed | April 4, 2026, 8:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d17b3559548190afe2bbf7cbf8a2af |
completed | April 4, 2026, 8:57 p.m. |
Created at: March 30, 2026, 8:10 p.m.