Triple
T7007642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Logan Lerman |
E162496
|
entity |
| Predicate | hasWorkedWith |
P9615
|
FINISHED |
| Object | Brad Pitt |
E42243
|
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: Brad Pitt | Statement: [Logan Lerman, hasWorkedWith, Brad Pitt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brad Pitt Context triple: [Logan Lerman, hasWorkedWith, Brad Pitt]
-
A.
Brad Pitt
chosen
Brad Pitt is an American actor and film producer renowned for his leading roles in major Hollywood films and for winning multiple Academy Awards.
-
B.
Ian Affleck
Ian Affleck is a Canadian theoretical physicist known for influential contributions to condensed matter physics and quantum field theory.
-
C.
Sean Penn
Sean Penn is an acclaimed American actor and filmmaker known for his intense, character-driven performances and multiple major awards, including two Academy Awards for Best Actor.
-
D.
Russell Crowe
Russell Crowe is an Academy Award–winning New Zealand–born actor renowned for intense, transformative performances in films such as Gladiator and A Beautiful Mind.
-
E.
Edward Norton
Edward Norton is an acclaimed American actor and filmmaker known for his intense, nuanced performances in films such as "Fight Club," "American History X," and "Birdman."
- 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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc36e3fc8190957445132a9ffb5f |
completed | March 27, 2026, 7:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a43c3a081909b9150d36ba107f5 |
completed | March 28, 2026, 5:42 a.m. |
Created at: March 27, 2026, 2:33 p.m.