Triple
T13541405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thackery Binx |
E323393
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Allison |
E483445
|
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: Allison | Statement: [Thackery Binx, associatedWith, Allison]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allison Context triple: [Thackery Binx, associatedWith, Allison]
-
A.
Allison
chosen
Allison is a common English-language surname of Scottish and Irish origin, often derived from "son of Alice" or "son of Alan."
-
B.
Alison
Alison is a feminine given name of English origin, commonly used in many English-speaking countries.
-
C.
Alison
Alison is a central character in Alan Garner's supernatural novel "The Owl Service," whose experiences drive the story's exploration of Welsh myth and identity.
-
D.
Alyssa
Alyssa is a feminine given name that gained widespread popularity in the late 20th century, partly due to its use by American actress Alyssa Milano.
-
E.
Alison Grace
Alison Grace is a film editor best known for her work on the family sports movie "Air Bud."
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafd8ba10819098faadcc6adf251e |
completed | April 12, 2026, 2:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78adaf62c8190855df932eb9830be |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:45 p.m.