Triple
T6503723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Among the Living |
E148953
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Lester Cole |
E467711
|
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: Lester Cole | Statement: [Among the Living, screenwriter, Lester Cole]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lester Cole Context triple: [Among the Living, screenwriter, Lester Cole]
-
A.
Lester Cole
chosen
Lester Cole was an American screenwriter and one of the Hollywood Ten, blacklisted during the Red Scare for alleged communist affiliations.
-
B.
Lester
Lester is a surname of Irish origin borne by various notable individuals, including the diplomat Seán Lester.
-
C.
Lester
Lester is the central character in the 2016 puzzle-platform video game "Mekazoo" (also known as "Makers" in some regions), around whom the game's story and gameplay revolve.
-
D.
Lester
Lester is a small town located in Raleigh County in the southern part of West Virginia, United States.
-
E.
Lester
Lester is the given name of Lester B. Pearson, the Canadian diplomat, Nobel Peace Prize laureate, and 14th prime minister of Canada.
- 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_69c687e9ad288190bae5bcac9c8ac855 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c699647ad08190ac0bfd78907d0c3b |
completed | March 27, 2026, 2:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb3c524481909d9c822e928dc821 |
completed | March 27, 2026, 6:23 p.m. |
Created at: March 27, 2026, 1:42 p.m.