Triple
T2499995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katie Lucas |
E52440
|
entity |
| Predicate | portrayedCharacter |
P1668
|
FINISHED |
| Object |
Amee
Amee is a character portrayed by Katie Lucas, likely within a film or television production.
|
E273346
|
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: Amee | Statement: [Katie Lucas, portrayedCharacter, Amee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amee Context triple: [Katie Lucas, portrayedCharacter, Amee]
-
A.
Amreya
Amreya is a district within Egypt’s Alexandria region, known for its mix of industrial zones, residential areas, and proximity to the Mediterranean coast.
-
B.
Amel
Amel is a municipality in the predominantly German-speaking region of eastern Belgium, known for its rural character and location in the Ardennes.
-
C.
Amaala
Amaala is a luxury tourism and wellness destination being developed on Saudi Arabia’s Red Sea coast as part of the country’s economic diversification and high-end tourism strategy.
-
D.
Naima
Naima is a character in the gospel musical and film "Black Nativity," which reimagines the Nativity story through an African-American cultural and spiritual lens.
-
E.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
- 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: Amee Triple: [Katie Lucas, portrayedCharacter, Amee]
Generated description
Amee is a character portrayed by Katie Lucas, likely within a film or television production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Amee Target entity description: Amee is a character portrayed by Katie Lucas, likely within a film or television production.
-
A.
Amreya
Amreya is a district within Egypt’s Alexandria region, known for its mix of industrial zones, residential areas, and proximity to the Mediterranean coast.
-
B.
Amel
Amel is a municipality in the predominantly German-speaking region of eastern Belgium, known for its rural character and location in the Ardennes.
-
C.
Amaala
Amaala is a luxury tourism and wellness destination being developed on Saudi Arabia’s Red Sea coast as part of the country’s economic diversification and high-end tourism strategy.
-
D.
Naima
Naima is a character in the gospel musical and film "Black Nativity," which reimagines the Nativity story through an African-American cultural and spiritual lens.
-
E.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1afd86c81909181c4b45d4f8bc5 |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f9ed13c81909856db636bfb2e9e |
completed | March 9, 2026, 7:29 p.m. |
| NEDg | Description generation | batch_69af23a305a48190b457b1b66779b90d |
completed | March 9, 2026, 7:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af240855848190947190a662745b77 |
completed | March 9, 2026, 7:48 p.m. |
Created at: March 6, 2026, 9:46 p.m.