Triple
T15632311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trees Lounge |
E375844
|
entity |
| Predicate | hasTitle |
P38
|
FINISHED |
| Object | Trees Lounge |
E375844
|
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: Trees Lounge | Statement: [Trees Lounge, hasTitle, Trees Lounge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trees Lounge Context triple: [Trees Lounge, hasTitle, Trees Lounge]
-
A.
Trees Lounge
chosen
Trees Lounge is a 1996 independent drama film written, directed by, and starring Steve Buscemi, focusing on a down-and-out mechanic who spends his days drinking in a neighborhood bar.
-
B.
Trees
Trees is a well-known live music venue in Dallas, Texas, recognized for hosting a wide range of rock, metal, and alternative acts in an intimate club setting.
-
C.
Tree
Tree is a common surname that has been borne by various individuals, including those in English-speaking countries.
-
D.
Some Trees
"Some Trees" is an influential early poetry collection by American poet John Ashbery, noted for its innovative, abstract style and importance to postwar American poetry.
-
E.
SeeTree
SeeTree is an agricultural technology company that uses advanced data analytics and artificial intelligence to monitor and optimize the health and productivity of trees and orchards.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eb7338881909f3c430bb73f91d1 |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f472b648190b7cd532a1b16373e |
completed | May 9, 2026, 4:22 p.m. |
Created at: April 10, 2026, 4:14 a.m.