Triple
T8318903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | End of Watch |
E194777
|
entity |
| Predicate | precededBy |
P97
|
FINISHED |
| Object | Finders Keepers |
E507778
|
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: Finders Keepers | Statement: [End of Watch, precededBy, Finders Keepers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Finders Keepers Context triple: [End of Watch, precededBy, Finders Keepers]
-
A.
Finders Keepers
chosen
Finders Keepers is a crime thriller novel by Stephen King that follows an obsessed reader who murders a reclusive author and hoards his unpublished work, setting off a deadly chain of events years later.
-
B.
Dry Diggings
Dry Diggings was the early Gold Rush–era mining camp that later became the city of Placerville, California.
-
C.
Bring Em Out
"Bring Em Out" is a 2004 hip hop single by T.I., produced by Swizz Beatz, known for its energetic beat and prominent Jay-Z sample.
-
D.
Lost & Found
"Lost & Found" is a film featuring veteran American actress Marla Gibbs, known for her acclaimed work in television and cinema.
-
E.
Beggars and Choosers
Beggars and Choosers is an American satirical comedy-drama television series that offers a behind-the-scenes look at the television industry and network executives.
- 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_69ca82e7a8a88190a32bb5cc0feb012d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f648e10819081ad1fed870b2b86 |
completed | March 31, 2026, 8:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd9596891c81909296050d0a8117ca |
completed | April 1, 2026, 10 p.m. |
Created at: March 30, 2026, 5:55 p.m.