Triple
T11533710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monty |
E273490
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Monty |
E16948
|
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: Monty | Statement: [Monty, name, Monty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monty Context triple: [Monty, name, Monty]
-
A.
Monty
chosen
Monty is the nickname of British Field Marshal Bernard Law Montgomery, a prominent World War II commander best known for his leadership in the North African and European campaigns.
-
B.
Monty
Monty is the costumed biscuit-themed mascot of the Montgomery Biscuits Minor League Baseball team.
-
C.
Monty Says
Monty Says is the personal blog of Michael "Monty" Widenius, the original creator of the MySQL database system, where he shares insights on open-source databases and related technologies.
-
D.
Monty Bodkin
Monty Bodkin is a recurring P. G. Wodehouse character, a wealthy but often luckless young man entangled in romantic and employment mishaps across several comic novels.
-
E.
Monty Kipps
Monty Kipps is a conservative, Trinidadian-born academic and Christian intellectual who serves as a central foil to the liberal Belsey family in Zadie Smith’s novel "On Beauty."
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8839a2c7081909c285d1f6beb971c |
completed | April 10, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e68577f5ec8190a46687119d288a36 |
completed | April 20, 2026, 7:58 p.m. |
Created at: April 8, 2026, 9:37 p.m.