Triple
T3624329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle Gomez |
E76799
|
entity |
| Predicate | notableCharacter |
P1481
|
FINISHED |
| Object | Missy |
E90859
|
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: Missy | Statement: [Michelle Gomez, notableCharacter, Missy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Missy Context triple: [Michelle Gomez, notableCharacter, Missy]
-
A.
Missy
chosen
Missy is the female incarnation of the Master, a recurring Time Lord villain and nemesis of the Doctor in the British science fiction series Doctor Who.
-
B.
Misti
Misti is a prominent, snow-capped stratovolcano overlooking the city of Arequipa in southern Peru.
-
C.
Molly
Molly is the central female protagonist in the 1926 silent film "Sparrows," portrayed by Mary Pickford.
-
D.
Marylou
Marylou is a free-spirited, impulsive young woman who embodies the restless, hedonistic energy of the Beat Generation in Jack Kerouac’s novel "On the Road."
-
E.
Messy Mya
Messy Mya was a New Orleans bounce rapper, comedian, and internet personality known for his viral YouTube videos and influence on the city’s bounce music scene.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2d9845c8190ad65b2471000dfa0 |
completed | March 8, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4332260cc8190964a15bfee0a3b61 |
completed | March 13, 2026, 3:54 p.m. |
Created at: March 8, 2026, 3:23 p.m.