Triple
T5343090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Knight's Tale |
E123987
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Wat |
E396298
|
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: Wat | Statement: [A Knight's Tale, mainCharacter, Wat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wat Context triple: [A Knight's Tale, mainCharacter, Wat]
-
A.
Wat
chosen
Wat is a medieval English diminutive form of the given name Walter, historically used as a familiar or nickname.
-
B.
wal
"wal" is the ISO 639-2 language code for Wolaytta, an Omotic language spoken primarily in southwestern Ethiopia.
-
C.
Wiyot
The Wiyot are a Native American people indigenous to the Humboldt Bay region of northwestern California, known for their distinct language, coastal culture, and tragic history of the 1860 massacre on Indian Island.
-
D.
WAT
WAT is the National Rail station code for London Waterloo, one of the busiest and most important railway terminals in the United Kingdom.
-
E.
Walo
Walo was a precolonial West African kingdom in the lower Senegal River region, known as one of the successor states to the Wolof Empire.
- 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_69bd464be27081908807b40b75c1bbae |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85e86edc81908d87933db6489f91 |
completed | March 20, 2026, 5:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf18cc387c8190a9fe430fe5bb38ce |
completed | March 21, 2026, 10:16 p.m. |
Created at: March 20, 2026, 2:01 p.m.