Triple
T3084501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Foy |
E64337
|
entity |
| Predicate | hasRivalryWith |
P893
|
FINISHED |
| Object | Albert Stark |
E64335
|
NE FINISHED |
How this triple was built (2 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: Albert Stark | Statement: [Foy, hasRivalryWith, Albert Stark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Albert Stark Context triple: [Foy, hasRivalryWith, Albert Stark]
-
A.
Albert Stark
chosen
Albert Stark is the timid, unlucky sheep farmer protagonist of the comedy Western film "A Million Ways to Die in the West," portrayed by Seth MacFarlane.
-
B.
Tony Stark
Tony Stark is a fictional billionaire industrialist and genius inventor who becomes the armored superhero Iron Man in Marvel Comics and the Marvel Cinematic Universe.
-
C.
Joe Simmons
Joe Simmons is the son of American actor J.K. Simmons.
-
D.
Dr. Abraham Erskine
Dr. Abraham Erskine is a brilliant scientist in the Marvel universe best known for creating the Super-Soldier Serum that transformed Steve Rogers into Captain America.
-
E.
Carl Ellsworth
Carl Ellsworth is an American screenwriter known for writing suspense and thriller films such as "Red Eye" and "Disturbia."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1e98a1c8190b1dd4a0a47f7d6c6 |
completed | March 8, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b203607df081909499458d6608f0e6 |
completed | March 12, 2026, 12:05 a.m. |
Created at: March 8, 2026, 3:03 p.m.