Triple
T8629075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little St. Simons Island |
E204353
|
entity |
| Predicate | maxGuestPolicy |
P67682
|
FINISHED |
| Object | limited number of guests |
—
|
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: limited number of guests | Statement: [Little St. Simons Island, maxGuestPolicy, limited number of guests]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxGuestPolicy Context triple: [Little St. Simons Island, maxGuestPolicy, limited number of guests]
-
A.
accommodationPolicy
chosen
Indicates the rules or guidelines that govern how accommodations are provided, used, or managed in a given context.
-
B.
reservationPolicy
Indicates the rules or conditions governing how reservations are made, modified, or canceled between parties.
-
C.
maximumMembersOfSameParty
Indicates the highest number of individuals within a group who belong to the same political party.
-
D.
boardingPolicy
Indicates the rules or procedures governing how and in what order passengers are allowed to board a vehicle or vessel.
-
E.
maximumNumberOfKnightsAndLadies
Indicates the greatest allowable or observed count of entities classified as knights and ladies within a given context or scenario.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5730309081909a9a0256c9bf5f8f |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc455906f8819082edd79cb4a1cf28 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:27 p.m.