Triple
T12939894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LPMA |
E309608
|
entity |
| Predicate | hasSafetyReputation |
P107603
|
FINISHED |
| Object | operationally challenging airport |
—
|
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: operationally challenging airport | Statement: [LPMA, hasSafetyReputation, operationally challenging airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSafetyReputation Context triple: [LPMA, hasSafetyReputation, operationally challenging airport]
-
A.
securityReputation
Indicates the assessed trustworthiness or risk level associated with an entity’s security posture or behavior.
-
B.
hasBrandReputation
Indicates that an entity possesses a certain level or type of perceived quality, trustworthiness, or public image associated with its brand.
-
C.
haveReputation
Indicates that an entity is recognized or regarded in a certain way by others, reflecting its perceived character, quality, or status.
-
D.
hasPolicyReputationFor
Indicates that an entity is recognized or regarded in a particular way with respect to its policies or policy-related behavior.
-
E.
hasSafetyCertificate
Indicates that an entity possesses or has been granted a valid safety certificate.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97db69f548190a1a693bc0d6c191a |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e5811f481908178fac6d2e0efcd |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 5:43 p.m.