Triple
T31626594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | F Troop |
E807040
|
entity |
| Predicate | locationOfFictionalFort |
P84403
|
FINISHED |
| Object | Kansas Territory |
E311711
|
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: Kansas Territory | Statement: [F Troop, locationOfFictionalFort, Kansas Territory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locationOfFictionalFort Context triple: [F Troop, locationOfFictionalFort, Kansas Territory]
-
A.
fortressLocation
Indicates that a fortress is located at or in a specified place or geographic area.
-
B.
fortressLocatedOn
Indicates that a fortress is situated on or atop a specified geographic feature, structure, or location.
-
C.
locatedNearFiction
Indicates that one fictional entity or place is situated close to another within an imagined or narrative context.
-
D.
fictionalHeadquartersLocation
Indicates the place where a fictional organization, group, or entity is based or has its main headquarters within a fictional context.
-
E.
fictionalLocationAssociatedWith
chosen
Indicates a relationship where a fictional entity (such as a character, event, or work) is connected to or set in a particular fictional location.
- 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_69f348d7883c8190b6c13ab92b7ef076 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a01b241f7308190bedb7522cb5dc069 |
completed | May 11, 2026, 10:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2b945faf488190bc26e1f4d8276168 |
completed | June 12, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_6a01b19ff12c81908ee1b188b4a3e118 |
completed | May 11, 2026, 10:38 a.m. |
Created at: April 30, 2026, 10:43 p.m.