Triple
T25114067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Matador of the Five Towns |
E629077
|
entity |
| Predicate | hasFictionalToponym |
P21117
|
FINISHED |
| Object | Five Towns |
E1007013
|
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: Five Towns | Statement: [The Matador of the Five Towns, hasFictionalToponym, Five Towns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalToponym Context triple: [The Matador of the Five Towns, hasFictionalToponym, Five Towns]
-
A.
hasFictionalLocation
chosen
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
B.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
-
C.
hasNotableToponym
Indicates that an entity is associated with a place name that is particularly notable, distinctive, or significant.
-
D.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
E.
hasToponymy
Indicates a relationship where one entity possesses or is associated with the system, study, or set of place names (toponyms) of another entity.
- F. None of above.
Provenance (4 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_69e2ff3169d08190973b6061d5009abd |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f67c9fe7b48190b79b4041357edb49 |
completed | May 2, 2026, 10:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10ad3714788190abbc5ead47b2bd09 |
completed | May 22, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 18, 2026, 6:27 a.m.