Triple
T1341436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Model Cities Program |
E28471
|
entity |
| Predicate | numberOfParticipatingCities |
P27365
|
FINISHED |
| Object | approximately 150 |
—
|
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: approximately 150 | Statement: [Model Cities Program, numberOfParticipatingCities, approximately 150]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfParticipatingCities Context triple: [Model Cities Program, numberOfParticipatingCities, approximately 150]
-
A.
numberOfHostCities
Indicates the count of distinct cities that have hosted or will host a particular event or activity.
-
B.
hasParticipantCity
Indicates that a city is involved as a participant in an event, activity, or relationship.
-
C.
numberOfParticipatingNations
Indicates the total count of nations that take part in a specified event, activity, or context.
-
D.
hasCoHostCity
Indicates that an event is jointly hosted or organized by the specified city alongside one or more other cities.
-
E.
numberOfParticipants
Indicates the total count of entities involved in a particular event, activity, or relationship.
- F. None of above. chosen
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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c215fc008190b01fd8150b9f3b2a |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef3e8fc8190ac9a1ba9b5879483 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c06721488190ac7f6e012f21af3d |
completed | March 1, 2026, 10:40 p.m. |
Created at: March 1, 2026, 7:56 p.m.