Triple
T18933090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Megan Gill |
E463164
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object |
Beast
Beast is a film project on which Megan Gill contributed her professional editing expertise.
|
E1350144
|
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: Beast | Statement: [Megan Gill, workedOn, Beast]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beast Context triple: [Megan Gill, workedOn, Beast]
-
A.
Beast
Beast is a major character in the comic series and game "The Wolf Among Us," depicted as a reformed fairy-tale monster struggling to maintain his human façade and relationship with his wife, Beauty, in the gritty Fabletown community.
-
B.
Beast
Beast is a thriller novel by Peter Benchley that centers on a deadly giant squid terrorizing a coastal community.
-
C.
Beast
Beast is a brilliant mutant scientist and acrobatic fighter known for his blue-furred, beast-like appearance and long-standing membership in the X-Men and Avengers.
-
D.
Beast
Beast is the short name of the Brampton Beast, a former professional ice hockey team based in Brampton, Ontario, that competed in the ECHL.
-
E.
Beast
Beast is the cursed prince who transforms into a monstrous creature and must learn love and compassion to break the spell in Disney’s 2017 live-action adaptation of "Beauty and the Beast."
- 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: Beast Triple: [Megan Gill, workedOn, Beast]
Generated description
Beast is a film project on which Megan Gill contributed her professional editing expertise.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Beast Target entity description: Beast is a film project on which Megan Gill contributed her professional editing expertise.
-
A.
Beast
Beast is a 2022 Indian Tamil-language action film starring Vijay, directed by Nelson Dilipkumar and produced by Sun Pictures.
-
B.
Beast
Beast is the cursed prince who transforms into a monstrous creature and must learn love and compassion to break the spell in Disney’s 2017 live-action adaptation of "Beauty and the Beast."
-
C.
Beast
Beast is a major character in the comic series and game "The Wolf Among Us," depicted as a reformed fairy-tale monster struggling to maintain his human façade and relationship with his wife, Beauty, in the gritty Fabletown community.
-
D.
Beast
Beast is a brilliant mutant scientist and acrobatic fighter known for his blue-furred, beast-like appearance and long-standing membership in the X-Men and Avengers.
-
E.
Beast
Beast is a thriller novel by Peter Benchley that centers on a deadly giant squid terrorizing a coastal community.
- 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d3e498308190bd1594cca841199c |
completed | April 20, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a059122dff081909fb907b752a2934f |
completed | May 14, 2026, 9:08 a.m. |
| NEDg | Description generation | batch_6a0595f0ebd08190bcaa378ca15ae40f |
completed | May 14, 2026, 9:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05969a874c81909a1392c4268bad75 |
completed | May 14, 2026, 9:32 a.m. |
Created at: April 10, 2026, 11:59 a.m.