Triple
T36893610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jim, I’m Still Here |
E911823
|
entity |
| Predicate | hasOriginatingCity |
P1041
|
FINISHED |
| Object | London |
—
|
NE NERFINISHED |
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: London | Statement: [Jim, I’m Still Here, hasOriginatingCity, London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOriginatingCity Context triple: [Jim, I’m Still Here, hasOriginatingCity, London]
-
A.
hasOriginAirportCity
Indicates that an entity (such as a flight or trip) departs from or is associated with a specific origin airport located in a given city.
-
B.
hasOriginAirport
Indicates that something, typically a flight or journey, departs from or is associated with a specific origin airport.
-
C.
hasTypicalOriginAirport
Indicates that an entity, such as a flight route or airline service, is commonly or usually associated with a particular origin airport from which it typically departs.
-
D.
cityOfOriginal
chosen
Indicates the city from which something or someone originally comes or was first created or established.
-
E.
hasTargetCity
Indicates that something is directed toward, intended for, or specifically associated with a particular city as its target.
- 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_69f76e841b54819097e7fa768bbc70b2 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff069ec1348190815375c5c9e38404 |
completed | May 9, 2026, 10:04 a.m. |
| PD | Predicate disambiguation | batch_69ff05ba57f88190a45d20f18044e0fb |
completed | May 9, 2026, 10 a.m. |
Created at: May 3, 2026, 4:13 p.m.