Triple
T25566069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Highams Park railway station |
E640838
|
entity |
| Predicate | hasStaffPresence |
P45233
|
FINISHED |
| Object | yes |
—
|
LITERAL 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: yes | Statement: [Highams Park railway station, hasStaffPresence, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStaffPresence Context triple: [Highams Park railway station, hasStaffPresence, yes]
-
A.
hasHumanPresence
Indicates that humans are physically present in or occupying a given location, object, or context.
-
B.
hasStaffedHours
chosen
Indicates that specific hours or time periods are assigned during which staff are present and available.
-
C.
hasStaffingStatus
Indicates the current staffing condition or level associated with an entity, such as whether it is adequately, under-, or over-staffed.
-
D.
hasCharacterPresence
Indicates that a particular character appears or is present within a specified context, such as a scene, work, or medium.
-
E.
hasOccupancyStatus
Indicates the current usage or availability state of something, such as whether it is occupied, vacant, or otherwise in use.
- F. None of above.
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_69e75dc1beb08190bac7d76b8d6e7bc4 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a5fd8481909433e923c5e24e55 |
completed | May 3, 2026, 2:32 a.m. |
Created at: April 21, 2026, 3:49 p.m.