Triple
T12917006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viktor Navorski |
E309010
|
entity |
| Predicate | formsFriendshipWith |
P39937
|
FINISHED |
| Object |
Mulroy
Mulroy is a character from the film "The Terminal," known as one of the airport workers who befriends Viktor Navorski during his extended stay in the terminal.
|
E1008946
|
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: Mulroy | Statement: [Viktor Navorski, formsFriendshipWith, Mulroy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mulroy Context triple: [Viktor Navorski, formsFriendshipWith, Mulroy]
-
A.
MacAuliffe
MacAuliffe is a surname, a spelling variant of McAuliffe, of Irish origin.
-
B.
Trulaske
Trulaske is the commonly used name for the Robert J. Trulaske, Sr. College of Business at the University of Missouri, a business school offering undergraduate and graduate programs in fields such as accounting, finance, and management.
-
C.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
D.
LeRoy
LeRoy is the middle name of American political consultant and Republican strategist Lee Atwater.
-
E.
LeRoy
LeRoy is a masculine given name of French origin, commonly used in the United States.
- 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: Mulroy Triple: [Viktor Navorski, formsFriendshipWith, Mulroy]
Generated description
Mulroy is a character from the film "The Terminal," known as one of the airport workers who befriends Viktor Navorski during his extended stay in the terminal.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mulroy Target entity description: Mulroy is a character from the film "The Terminal," known as one of the airport workers who befriends Viktor Navorski during his extended stay in the terminal.
-
A.
MacAuliffe
MacAuliffe is a surname, a spelling variant of McAuliffe, of Irish origin.
-
B.
Trulaske
Trulaske is the commonly used name for the Robert J. Trulaske, Sr. College of Business at the University of Missouri, a business school offering undergraduate and graduate programs in fields such as accounting, finance, and management.
-
C.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
D.
LeRoy
LeRoy is the middle name of American political consultant and Republican strategist Lee Atwater.
-
E.
LeRoy
LeRoy is a masculine given name of French origin, commonly used in the United States.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a1e8088190af697629baecf59f |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a571a3e48190a32d362adc6eaee2 |
completed | May 3, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69f6a66cd21081909283d70e0f5d06cb |
completed | May 3, 2026, 1:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a77ba20081908f72f46f64382fca |
completed | May 3, 2026, 1:40 a.m. |
Created at: April 9, 2026, 5:41 p.m.