Triple
T22450914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jacob Tierney |
E554984
|
entity |
| Predicate | basedIn |
P40
|
FINISHED |
| Object | Montreal |
—
|
NE NERFINISHED |
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: Montreal | Statement: [Jacob Tierney, basedIn, Montreal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montreal Context triple: [Jacob Tierney, basedIn, Montreal]
-
A.
Montreal
chosen
Montreal is the largest city in Quebec, Canada, known for its vibrant bilingual culture, historic architecture, and status as a major economic and cultural center.
-
B.
Montreal
"Montreal" is a song by the American metal band Ataxia.
-
C.
Montreal
Montreal was a prominent Crusader-era fortress in the Lordship of Oultrejordain, strategically controlling key trade and pilgrimage routes east of the Jordan River.
-
D.
Quebec City
Quebec City is the historic capital of the Canadian province of Quebec, renowned for its well-preserved fortified old town and rich French colonial heritage.
-
E.
Québec-Montréal
Québec-Montréal is a Canadian film that follows a group of thirty-somethings on a road trip between Quebec City and Montreal as they confront their relationships, regrets, and life choices.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b4ba6a88190a0a79e2c20fa8c08 |
completed | April 29, 2026, 1:13 a.m. |
Created at: April 16, 2026, 8:48 p.m.