Triple
T2557388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sebastian Faulks |
E56757
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Faulks |
E56757
|
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: Faulks | Statement: [Sebastian Faulks, familyName, Faulks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Faulks Context triple: [Sebastian Faulks, familyName, Faulks]
-
A.
Faulks
chosen
Faulks is the surname of British novelist and journalist Sebastian Faulks, best known for his historical and literary fiction.
-
B.
Falkner
Falkner is a lesser-known 1837 novel by Mary Shelley that explores themes of guilt, redemption, and complex family relationships.
-
C.
Fermor
Fermor is a variant form of the surname "Farmer," historically associated with individuals who worked the land or managed agricultural estates.
-
D.
Outram
Outram is an English surname historically associated with several notable figures, including British military officers and public servants.
-
E.
Outram
Outram is a central district in Singapore known for its major medical facilities, heritage architecture, and proximity to the downtown core.
- 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_69ab4a4bfec081908039988ec4c86e28 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd310b3a48190b275be13eb050e57 |
completed | March 7, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5d1d937c8190ac6b7bb405a49c3c |
completed | March 9, 2026, 11:51 p.m. |
Created at: March 6, 2026, 9:48 p.m.