Triple
T1584985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liz Imbrie |
E34043
|
entity |
| Predicate | romanticInterestIn |
P7325
|
FINISHED |
| Object | Mike Connor |
E28982
|
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: Mike Connor | Statement: [Liz Imbrie, romanticInterestIn, Mike Connor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Connor Context triple: [Liz Imbrie, romanticInterestIn, Mike Connor]
-
A.
Mike Connor
chosen
Mike Connor is a cynical yet principled reporter who becomes romantically entangled with the wealthy socialite heroine in the classic film "High Society."
-
B.
Jeff Maggioncalda
Jeff Maggioncalda is a business executive best known for leading the online learning platform Coursera as its chief executive officer.
-
C.
Martin Connor
Martin Connor is a film editor known for his work on the biographical war drama "The Railway Man."
-
D.
Bryan Donovan
Bryan Donovan is one of the children of American basketball coach Billy Donovan.
-
E.
Brad Daugherty
Brad Daugherty is a former NBA center best known for his All-Star career with the Cleveland Cavaliers in the late 1980s and early 1990s.
- 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_69a885fceb2c8190b47e0f7c0aefbff0 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a908f240708190a76bb642fc6a6f42 |
completed | March 5, 2026, 4:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8ab5e8b88190b95af87391e5bb16 |
completed | March 8, 2026, 2:41 p.m. |
Created at: March 4, 2026, 7:27 p.m.