Triple
T6333975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Cooper |
E142445
|
entity |
| Predicate | allies |
P2865
|
FINISHED |
| Object | TARS |
E142450
|
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: TARS | Statement: [Joseph Cooper, allies, TARS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TARS Context triple: [Joseph Cooper, allies, TARS]
-
A.
TARS
chosen
TARS is a witty, modular, and highly capable robotic assistant featured in the science fiction film "Interstellar."
-
B.
Tars
Tars is the athletic nickname for the sports teams representing Rollins College.
-
C.
Tarskavaig
Tarskavaig is a small coastal crofting village on the Sleat peninsula of the Isle of Skye in Scotland, known for its scenic bay and traditional Gaelic heritage.
-
D.
Tayk
Tayk was an ancient historical region in the southwestern Caucasus, associated with Armenian and later Georgian polities and known for its strategic location and mountainous terrain.
-
E.
Tetro
Tetro is a 2009 drama film directed by Francis Ford Coppola, in which Maribel Verdú plays a key supporting role in a story about fractured family relationships and artistic rivalry in Buenos Aires.
- 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_69c008d4d8e88190ad301c05b08722ac |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06549084c8190b73fd94c9e0cb302 |
completed | March 22, 2026, 9:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d412bc88190aa8ee0a40ec8bc30 |
completed | March 27, 2026, 7:09 a.m. |
Created at: March 22, 2026, 4:30 p.m.